{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "\n",
    "environ['optimizer'] = 'Adam'\n",
    "environ['num_workers']= '2'\n",
    "environ['batch_size']= str(2048)\n",
    "environ['n_epochs']= '1200'\n",
    "environ['batch_norm']= 'True'\n",
    "environ['loss_func']='MSE'\n",
    "environ['layers'] = '800 700 600 350 200 180'\n",
    "environ['dropouts'] = '0.11 '* 6\n",
    "environ['lr'] = '1e-03'\n",
    "environ['log'] = 'False'\n",
    "environ['weight_decay'] = '0.011'\n",
    "environ['cuda_device'] ='cuda:6'\n",
    "environ['dataset'] = 'data/speedup_dataset2.pkl'\n",
    "\n",
    "%run utils.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "function329_schedule_13\n",
      "0\n",
      "{'computations': {'computations_array': [{'comp_id': 1,\n",
      "                                          'lhs_data_type': 'p_int32',\n",
      "                                          'loop_iterators_ids': [2, 3],\n",
      "                                          'operations_histogram': [[5, 3, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0,\n",
      "                                                                    0,\n",
      "                                                                    0,\n",
      "                                                                    0]],\n",
      "                                          'rhs_accesses': {'accesses': [{'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     0],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     0],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     -1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     0]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     -1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     -1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     0]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     -1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     -1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     -1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     0],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     0]],\n",
      "                                                                         'comp_id': 0}],\n",
      "                                                           'n': 9}}],\n",
      "                  'n': 1},\n",
      " 'inputs': {'inputs_array': [{'data_type': 'p_int32',\n",
      "                              'input_id': 0,\n",
      "                              'loop_iterators_ids': [0, 1]}],\n",
      "            'n': 1},\n",
      " 'iterators': {'iterators_array': [{'it_id': 2,\n",
      "                                    'lower_bound': 1,\n",
      "                                    'upper_bound': 1048575},\n",
      "                                   {'it_id': 3,\n",
      "                                    'lower_bound': 1,\n",
      "                                    'upper_bound': 63},\n",
      "                                   {'it_id': 0,\n",
      "                                    'lower_bound': 0,\n",
      "                                    'upper_bound': 1048576},\n",
      "                                   {'it_id': 1,\n",
      "                                    'lower_bound': 0,\n",
      "                                    'upper_bound': 64}],\n",
      "               'n': 4},\n",
      " 'loops': {'loops_array': [{'assignments': {'assignments_array': [], 'n': 0},\n",
      "                            'loop_id': 0,\n",
      "                            'loop_it': 2,\n",
      "                            'parent': -1,\n",
      "                            'position': 0},\n",
      "                           {'assignments': {'assignments_array': [{'id': 1,\n",
      "                                                                   'position': 0}],\n",
      "                                            'n': 1},\n",
      "                            'loop_id': 1,\n",
      "                            'loop_it': 3,\n",
      "                            'parent': 0,\n",
      "                            'position': 0}],\n",
      "           'n': 2},\n",
      " 'seed': 329,\n",
      " 'type': 2}\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'exit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36mtile\u001b[0;34m(self, loop_id, factor)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mloop_id\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m                 \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Computation' object has no attribute 'iterator'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-ccbb4c277821>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_dev_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasic_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataBunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-38-fa1393f8fd16>\u001b[0m in \u001b[0;36mtrain_dev_split\u001b[0;34m(dataset, batch_size, num_workers, log, seed)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatasetFromPkl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/dataset.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename, normalized, log, maxsize)\u001b[0m\n\u001b[1;32m    102\u001b[0m             \u001b[0mprogram\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprograms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogram_indexes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprogram\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36madd_schedule\u001b[0;34m(self, schedule)\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mLoop_AST\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict_repr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdtype_to_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, dict_repr, schedule)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36mapply_schedule\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtype_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tiling'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbinary_schedule\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mloop_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtype_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'interchange'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbinary_schedule\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36mtile\u001b[0;34m(self, loop_id, factor)\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpprint\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0mpprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict_repr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m             \u001b[0mexit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'exit' is not defined"
     ]
    }
   ],
   "source": [
    "train_dl, val_dl, test_dl = train_dev_split(dataset, batch_size, num_workers, log=log)\n",
    "\n",
    "db = fai.basic_data.DataBunch(train_dl, val_dl, test_dl, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = train_dl.dataset.X.shape[1]\n",
    "output_size = train_dl.dataset.Y.shape[1]\n",
    "\n",
    "\n",
    "model = None \n",
    "\n",
    "if batch_norm:\n",
    "    model = Model_BN(input_size, output_size, hidden_sizes=layers_sizes, drops=drops)\n",
    "else:\n",
    "    model = Model(input_size, output_size)\n",
    "\n",
    "if loss_func == 'MSE':\n",
    "    criterion = nn.MSELoss()\n",
    "elif loss_func == 'MAPE':\n",
    "    criterion = mape_criterion\n",
    "elif loss_func == 'SMAPE':\n",
    "    criterion = smape_criterion\n",
    "\n",
    "l = fai.Learner(db, model, loss_func=criterion, metrics=[mape_criterion, rmse_criterion])\n",
    "\n",
    "if optimizer == 'SGD':\n",
    "    l.opt_func = optim.SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = l.load(f\"r_speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}_log_{log}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.lr_find()\n",
    "l.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 1:10:08 <p><table style='width:375px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>mape_criterion</th>\n",
       "    <th>rmse_criterion</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>3.119877</th>\n",
       "    <th>4.159297</th>\n",
       "    <th>103.162346</th>\n",
       "    <th>2.038654</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>2.458937</th>\n",
       "    <th>3.360072</th>\n",
       "    <th>151.471848</th>\n",
       "    <th>1.832884</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>2.052949</th>\n",
       "    <th>2.909236</th>\n",
       "    <th>183.127594</th>\n",
       "    <th>1.704980</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>1.763731</th>\n",
       "    <th>2.477645</th>\n",
       "    <th>166.634384</th>\n",
       "    <th>1.572941</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>1.668764</th>\n",
       "    <th>2.382118</th>\n",
       "    <th>159.341629</th>\n",
       "    <th>1.542754</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>1.627714</th>\n",
       "    <th>2.324363</th>\n",
       "    <th>149.311172</th>\n",
       "    <th>1.524337</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>1.576635</th>\n",
       "    <th>2.319100</th>\n",
       "    <th>138.422226</th>\n",
       "    <th>1.521556</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>1.531117</th>\n",
       "    <th>2.168367</th>\n",
       "    <th>143.406693</th>\n",
       "    <th>1.472214</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>1.482670</th>\n",
       "    <th>2.128766</th>\n",
       "    <th>139.532303</th>\n",
       "    <th>1.457496</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>1.464806</th>\n",
       "    <th>2.069212</th>\n",
       "    <th>141.572311</th>\n",
       "    <th>1.438002</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>1.419815</th>\n",
       "    <th>1.908513</th>\n",
       "    <th>145.050201</th>\n",
       "    <th>1.380745</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>1.388305</th>\n",
       "    <th>1.892902</th>\n",
       "    <th>142.724518</th>\n",
       "    <th>1.375559</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>1.372419</th>\n",
       "    <th>1.945301</th>\n",
       "    <th>140.964157</th>\n",
       "    <th>1.394101</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>1.347962</th>\n",
       "    <th>1.836970</th>\n",
       "    <th>141.336197</th>\n",
       "    <th>1.354999</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>1.352326</th>\n",
       "    <th>1.889487</th>\n",
       "    <th>146.588943</th>\n",
       "    <th>1.374369</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>1.309006</th>\n",
       "    <th>1.726306</th>\n",
       "    <th>143.368561</th>\n",
       "    <th>1.313716</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>1.285257</th>\n",
       "    <th>1.807702</th>\n",
       "    <th>139.191101</th>\n",
       "    <th>1.343356</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>1.273544</th>\n",
       "    <th>1.670614</th>\n",
       "    <th>147.955048</th>\n",
       "    <th>1.291968</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>1.252550</th>\n",
       "    <th>1.799533</th>\n",
       "    <th>137.228226</th>\n",
       "    <th>1.341073</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>1.234485</th>\n",
       "    <th>1.657880</th>\n",
       "    <th>142.605225</th>\n",
       "    <th>1.287345</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>21</th>\n",
       "    <th>1.231580</th>\n",
       "    <th>1.725841</th>\n",
       "    <th>139.279434</th>\n",
       "    <th>1.313315</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>22</th>\n",
       "    <th>1.210994</th>\n",
       "    <th>1.812722</th>\n",
       "    <th>138.140488</th>\n",
       "    <th>1.346266</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>23</th>\n",
       "    <th>1.180092</th>\n",
       "    <th>1.626870</th>\n",
       "    <th>132.472198</th>\n",
       "    <th>1.274803</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>24</th>\n",
       "    <th>1.183105</th>\n",
       "    <th>1.816368</th>\n",
       "    <th>131.703018</th>\n",
       "    <th>1.346385</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>25</th>\n",
       "    <th>1.168462</th>\n",
       "    <th>1.933239</th>\n",
       "    <th>125.115196</th>\n",
       "    <th>1.389659</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>26</th>\n",
       "    <th>1.157859</th>\n",
       "    <th>1.678803</th>\n",
       "    <th>131.229202</th>\n",
       "    <th>1.294858</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>27</th>\n",
       "    <th>1.113932</th>\n",
       "    <th>1.617365</th>\n",
       "    <th>131.479263</th>\n",
       "    <th>1.271243</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>28</th>\n",
       "    <th>1.097278</th>\n",
       "    <th>1.659424</th>\n",
       "    <th>131.024597</th>\n",
       "    <th>1.288021</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>29</th>\n",
       "    <th>1.065781</th>\n",
       "    <th>1.623170</th>\n",
       "    <th>129.736969</th>\n",
       "    <th>1.273783</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>30</th>\n",
       "    <th>1.048911</th>\n",
       "    <th>1.515324</th>\n",
       "    <th>130.019119</th>\n",
       "    <th>1.229141</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>31</th>\n",
       "    <th>1.075112</th>\n",
       "    <th>1.613534</th>\n",
       "    <th>136.784012</th>\n",
       "    <th>1.269187</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>32</th>\n",
       "    <th>1.059132</th>\n",
       "    <th>1.761956</th>\n",
       "    <th>128.306671</th>\n",
       "    <th>1.327267</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>33</th>\n",
       "    <th>1.027710</th>\n",
       "    <th>1.829119</th>\n",
       "    <th>125.620651</th>\n",
       "    <th>1.351971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>34</th>\n",
       "    <th>1.026860</th>\n",
       "    <th>1.664926</th>\n",
       "    <th>125.301064</th>\n",
       "    <th>1.290217</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>35</th>\n",
       "    <th>0.996929</th>\n",
       "    <th>1.740414</th>\n",
       "    <th>124.529526</th>\n",
       "    <th>1.318961</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>36</th>\n",
       "    <th>0.999108</th>\n",
       "    <th>1.623054</th>\n",
       "    <th>124.079247</th>\n",
       "    <th>1.272657</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>37</th>\n",
       "    <th>0.964611</th>\n",
       "    <th>1.539746</th>\n",
       "    <th>130.963364</th>\n",
       "    <th>1.240497</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>38</th>\n",
       "    <th>0.933684</th>\n",
       "    <th>1.429818</th>\n",
       "    <th>122.551216</th>\n",
       "    <th>1.195192</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>39</th>\n",
       "    <th>0.992827</th>\n",
       "    <th>1.536408</th>\n",
       "    <th>129.641159</th>\n",
       "    <th>1.239398</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>40</th>\n",
       "    <th>0.981773</th>\n",
       "    <th>1.465253</th>\n",
       "    <th>129.942169</th>\n",
       "    <th>1.209506</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>41</th>\n",
       "    <th>0.947084</th>\n",
       "    <th>1.402562</th>\n",
       "    <th>127.626076</th>\n",
       "    <th>1.183241</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>42</th>\n",
       "    <th>0.966133</th>\n",
       "    <th>1.578613</th>\n",
       "    <th>131.963486</th>\n",
       "    <th>1.256302</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>43</th>\n",
       "    <th>0.952330</th>\n",
       "    <th>1.420017</th>\n",
       "    <th>126.452904</th>\n",
       "    <th>1.190990</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>44</th>\n",
       "    <th>0.925124</th>\n",
       "    <th>1.693338</th>\n",
       "    <th>124.465675</th>\n",
       "    <th>1.301196</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>45</th>\n",
       "    <th>0.909009</th>\n",
       "    <th>1.589748</th>\n",
       "    <th>133.088425</th>\n",
       "    <th>1.260656</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>46</th>\n",
       "    <th>0.871879</th>\n",
       "    <th>1.480783</th>\n",
       "    <th>128.473282</th>\n",
       "    <th>1.216144</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>47</th>\n",
       "    <th>0.845078</th>\n",
       "    <th>1.808878</th>\n",
       "    <th>125.664925</th>\n",
       "    <th>1.344234</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>48</th>\n",
       "    <th>0.809716</th>\n",
       "    <th>1.810237</th>\n",
       "    <th>126.078712</th>\n",
       "    <th>1.345164</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>49</th>\n",
       "    <th>0.813880</th>\n",
       "    <th>1.909651</th>\n",
       "    <th>125.119987</th>\n",
       "    <th>1.381511</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>50</th>\n",
       "    <th>0.798009</th>\n",
       "    <th>2.087227</th>\n",
       "    <th>124.800697</th>\n",
       "    <th>1.443841</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>51</th>\n",
       "    <th>0.803258</th>\n",
       "    <th>2.310949</th>\n",
       "    <th>126.656754</th>\n",
       "    <th>1.519828</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>52</th>\n",
       "    <th>0.793663</th>\n",
       "    <th>1.842795</th>\n",
       "    <th>120.894127</th>\n",
       "    <th>1.356635</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>53</th>\n",
       "    <th>0.748131</th>\n",
       "    <th>1.998771</th>\n",
       "    <th>126.817635</th>\n",
       "    <th>1.412342</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>54</th>\n",
       "    <th>0.774604</th>\n",
       "    <th>2.190444</th>\n",
       "    <th>123.942825</th>\n",
       "    <th>1.479187</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>55</th>\n",
       "    <th>0.779247</th>\n",
       "    <th>1.846167</th>\n",
       "    <th>124.731285</th>\n",
       "    <th>1.358030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>56</th>\n",
       "    <th>0.725704</th>\n",
       "    <th>2.059845</th>\n",
       "    <th>123.273560</th>\n",
       "    <th>1.434904</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>57</th>\n",
       "    <th>0.720434</th>\n",
       "    <th>1.765807</th>\n",
       "    <th>117.120659</th>\n",
       "    <th>1.328202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>58</th>\n",
       "    <th>0.720329</th>\n",
       "    <th>2.041100</th>\n",
       "    <th>118.386749</th>\n",
       "    <th>1.427928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>59</th>\n",
       "    <th>0.690613</th>\n",
       "    <th>2.042965</th>\n",
       "    <th>120.170677</th>\n",
       "    <th>1.428296</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>60</th>\n",
       "    <th>0.693025</th>\n",
       "    <th>1.801416</th>\n",
       "    <th>115.312874</th>\n",
       "    <th>1.342025</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>61</th>\n",
       "    <th>0.714972</th>\n",
       "    <th>1.930613</th>\n",
       "    <th>121.848785</th>\n",
       "    <th>1.388965</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>62</th>\n",
       "    <th>0.725971</th>\n",
       "    <th>2.227051</th>\n",
       "    <th>117.441597</th>\n",
       "    <th>1.490409</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>63</th>\n",
       "    <th>0.730101</th>\n",
       "    <th>2.441448</th>\n",
       "    <th>120.224472</th>\n",
       "    <th>1.561794</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>64</th>\n",
       "    <th>0.714172</th>\n",
       "    <th>1.906287</th>\n",
       "    <th>112.693214</th>\n",
       "    <th>1.379779</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>65</th>\n",
       "    <th>0.727435</th>\n",
       "    <th>1.441927</th>\n",
       "    <th>117.230087</th>\n",
       "    <th>1.200135</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>66</th>\n",
       "    <th>0.697677</th>\n",
       "    <th>1.783687</th>\n",
       "    <th>116.422325</th>\n",
       "    <th>1.335412</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>67</th>\n",
       "    <th>0.691600</th>\n",
       "    <th>2.100202</th>\n",
       "    <th>128.758087</th>\n",
       "    <th>1.448871</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>68</th>\n",
       "    <th>0.679232</th>\n",
       "    <th>1.931315</th>\n",
       "    <th>116.228691</th>\n",
       "    <th>1.389670</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>69</th>\n",
       "    <th>0.666700</th>\n",
       "    <th>1.783789</th>\n",
       "    <th>107.531914</th>\n",
       "    <th>1.334751</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>70</th>\n",
       "    <th>0.671331</th>\n",
       "    <th>1.691642</th>\n",
       "    <th>119.705940</th>\n",
       "    <th>1.300142</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>71</th>\n",
       "    <th>0.662158</th>\n",
       "    <th>1.939294</th>\n",
       "    <th>114.952324</th>\n",
       "    <th>1.390400</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>72</th>\n",
       "    <th>0.642722</th>\n",
       "    <th>1.552013</th>\n",
       "    <th>106.849060</th>\n",
       "    <th>1.245666</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>73</th>\n",
       "    <th>0.647633</th>\n",
       "    <th>1.749076</th>\n",
       "    <th>113.505035</th>\n",
       "    <th>1.322230</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>74</th>\n",
       "    <th>0.642517</th>\n",
       "    <th>1.775123</th>\n",
       "    <th>120.564323</th>\n",
       "    <th>1.332165</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>75</th>\n",
       "    <th>0.635705</th>\n",
       "    <th>2.019512</th>\n",
       "    <th>111.315186</th>\n",
       "    <th>1.420244</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>76</th>\n",
       "    <th>0.637294</th>\n",
       "    <th>1.873256</th>\n",
       "    <th>107.001228</th>\n",
       "    <th>1.368568</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>77</th>\n",
       "    <th>0.646236</th>\n",
       "    <th>2.115181</th>\n",
       "    <th>107.304977</th>\n",
       "    <th>1.453196</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>78</th>\n",
       "    <th>0.646668</th>\n",
       "    <th>1.841369</th>\n",
       "    <th>110.191704</th>\n",
       "    <th>1.356517</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>79</th>\n",
       "    <th>0.620576</th>\n",
       "    <th>1.753481</th>\n",
       "    <th>107.081078</th>\n",
       "    <th>1.323045</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>80</th>\n",
       "    <th>0.629455</th>\n",
       "    <th>1.880888</th>\n",
       "    <th>106.930351</th>\n",
       "    <th>1.370908</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>81</th>\n",
       "    <th>0.621386</th>\n",
       "    <th>1.682623</th>\n",
       "    <th>109.700737</th>\n",
       "    <th>1.295767</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>82</th>\n",
       "    <th>0.623757</th>\n",
       "    <th>2.241790</th>\n",
       "    <th>105.876648</th>\n",
       "    <th>1.496994</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>83</th>\n",
       "    <th>0.606325</th>\n",
       "    <th>1.865404</th>\n",
       "    <th>113.165451</th>\n",
       "    <th>1.363879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>84</th>\n",
       "    <th>0.587264</th>\n",
       "    <th>1.744814</th>\n",
       "    <th>100.405342</th>\n",
       "    <th>1.320108</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>85</th>\n",
       "    <th>0.625200</th>\n",
       "    <th>1.658385</th>\n",
       "    <th>101.940125</th>\n",
       "    <th>1.287161</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>86</th>\n",
       "    <th>0.598585</th>\n",
       "    <th>1.828812</th>\n",
       "    <th>119.196465</th>\n",
       "    <th>1.351268</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>87</th>\n",
       "    <th>0.601319</th>\n",
       "    <th>1.849705</th>\n",
       "    <th>113.748047</th>\n",
       "    <th>1.359325</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>88</th>\n",
       "    <th>0.611904</th>\n",
       "    <th>1.893726</th>\n",
       "    <th>100.104897</th>\n",
       "    <th>1.375888</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>89</th>\n",
       "    <th>0.657705</th>\n",
       "    <th>2.001693</th>\n",
       "    <th>107.370010</th>\n",
       "    <th>1.413228</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>90</th>\n",
       "    <th>0.560772</th>\n",
       "    <th>1.701046</th>\n",
       "    <th>94.244247</th>\n",
       "    <th>1.303616</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>91</th>\n",
       "    <th>0.563263</th>\n",
       "    <th>1.903952</th>\n",
       "    <th>103.675331</th>\n",
       "    <th>1.379429</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>92</th>\n",
       "    <th>0.589511</th>\n",
       "    <th>1.639474</th>\n",
       "    <th>107.521523</th>\n",
       "    <th>1.279435</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>93</th>\n",
       "    <th>0.580688</th>\n",
       "    <th>1.727988</th>\n",
       "    <th>94.814896</th>\n",
       "    <th>1.314033</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>94</th>\n",
       "    <th>0.561698</th>\n",
       "    <th>1.555581</th>\n",
       "    <th>109.183334</th>\n",
       "    <th>1.246795</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>95</th>\n",
       "    <th>0.560054</th>\n",
       "    <th>1.541789</th>\n",
       "    <th>98.742035</th>\n",
       "    <th>1.241515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>96</th>\n",
       "    <th>0.572685</th>\n",
       "    <th>1.560505</th>\n",
       "    <th>91.998337</th>\n",
       "    <th>1.248443</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>97</th>\n",
       "    <th>0.550977</th>\n",
       "    <th>1.392285</th>\n",
       "    <th>93.477066</th>\n",
       "    <th>1.179142</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>98</th>\n",
       "    <th>0.525389</th>\n",
       "    <th>1.276214</th>\n",
       "    <th>96.099472</th>\n",
       "    <th>1.128984</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>99</th>\n",
       "    <th>0.574497</th>\n",
       "    <th>1.651492</th>\n",
       "    <th>101.072899</th>\n",
       "    <th>1.284708</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>100</th>\n",
       "    <th>0.520334</th>\n",
       "    <th>1.454352</th>\n",
       "    <th>88.660294</th>\n",
       "    <th>1.205364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>101</th>\n",
       "    <th>0.575967</th>\n",
       "    <th>1.275921</th>\n",
       "    <th>94.958481</th>\n",
       "    <th>1.129089</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>102</th>\n",
       "    <th>0.508792</th>\n",
       "    <th>1.583715</th>\n",
       "    <th>83.560585</th>\n",
       "    <th>1.258188</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>103</th>\n",
       "    <th>0.492444</th>\n",
       "    <th>1.565348</th>\n",
       "    <th>86.927773</th>\n",
       "    <th>1.250888</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>104</th>\n",
       "    <th>0.497619</th>\n",
       "    <th>1.439216</th>\n",
       "    <th>88.708839</th>\n",
       "    <th>1.199178</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>105</th>\n",
       "    <th>0.495516</th>\n",
       "    <th>1.527188</th>\n",
       "    <th>89.432739</th>\n",
       "    <th>1.235717</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>106</th>\n",
       "    <th>0.473811</th>\n",
       "    <th>1.354015</th>\n",
       "    <th>85.955040</th>\n",
       "    <th>1.162211</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>107</th>\n",
       "    <th>0.507704</th>\n",
       "    <th>1.518998</th>\n",
       "    <th>91.637001</th>\n",
       "    <th>1.232058</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>108</th>\n",
       "    <th>0.470779</th>\n",
       "    <th>1.143936</th>\n",
       "    <th>77.236237</th>\n",
       "    <th>1.069528</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>109</th>\n",
       "    <th>0.447169</th>\n",
       "    <th>1.508342</th>\n",
       "    <th>76.214355</th>\n",
       "    <th>1.227927</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>110</th>\n",
       "    <th>0.450888</th>\n",
       "    <th>1.303494</th>\n",
       "    <th>76.073448</th>\n",
       "    <th>1.141683</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>111</th>\n",
       "    <th>0.477067</th>\n",
       "    <th>1.572500</th>\n",
       "    <th>82.641113</th>\n",
       "    <th>1.253288</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>112</th>\n",
       "    <th>0.451103</th>\n",
       "    <th>1.294602</th>\n",
       "    <th>83.325600</th>\n",
       "    <th>1.137023</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>113</th>\n",
       "    <th>0.520759</th>\n",
       "    <th>1.368117</th>\n",
       "    <th>89.187782</th>\n",
       "    <th>1.169408</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>114</th>\n",
       "    <th>0.469473</th>\n",
       "    <th>1.216594</th>\n",
       "    <th>89.349060</th>\n",
       "    <th>1.102633</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>115</th>\n",
       "    <th>0.553151</th>\n",
       "    <th>1.224327</th>\n",
       "    <th>95.070930</th>\n",
       "    <th>1.106451</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>116</th>\n",
       "    <th>0.457413</th>\n",
       "    <th>1.175548</th>\n",
       "    <th>78.910126</th>\n",
       "    <th>1.083967</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>117</th>\n",
       "    <th>0.423293</th>\n",
       "    <th>1.265560</th>\n",
       "    <th>78.267319</th>\n",
       "    <th>1.124370</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>118</th>\n",
       "    <th>0.437232</th>\n",
       "    <th>1.092538</th>\n",
       "    <th>76.999428</th>\n",
       "    <th>1.044520</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>119</th>\n",
       "    <th>0.521231</th>\n",
       "    <th>1.223347</th>\n",
       "    <th>86.320915</th>\n",
       "    <th>1.105628</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>120</th>\n",
       "    <th>0.533385</th>\n",
       "    <th>1.082803</th>\n",
       "    <th>81.785019</th>\n",
       "    <th>1.040331</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>121</th>\n",
       "    <th>0.498349</th>\n",
       "    <th>0.901337</th>\n",
       "    <th>97.241203</th>\n",
       "    <th>0.949236</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>122</th>\n",
       "    <th>0.434286</th>\n",
       "    <th>0.986848</th>\n",
       "    <th>91.683472</th>\n",
       "    <th>0.993010</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>123</th>\n",
       "    <th>0.464103</th>\n",
       "    <th>1.172653</th>\n",
       "    <th>110.596703</th>\n",
       "    <th>1.082308</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>124</th>\n",
       "    <th>0.440203</th>\n",
       "    <th>0.981711</th>\n",
       "    <th>74.530876</th>\n",
       "    <th>0.990728</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>125</th>\n",
       "    <th>0.425814</th>\n",
       "    <th>1.198924</th>\n",
       "    <th>78.298599</th>\n",
       "    <th>1.093840</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>126</th>\n",
       "    <th>0.421047</th>\n",
       "    <th>1.244012</th>\n",
       "    <th>93.545448</th>\n",
       "    <th>1.115153</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>127</th>\n",
       "    <th>0.416230</th>\n",
       "    <th>1.070482</th>\n",
       "    <th>84.104942</th>\n",
       "    <th>1.034373</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>128</th>\n",
       "    <th>0.449729</th>\n",
       "    <th>1.112025</th>\n",
       "    <th>111.098602</th>\n",
       "    <th>1.052973</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>129</th>\n",
       "    <th>0.447012</th>\n",
       "    <th>0.943311</th>\n",
       "    <th>97.194847</th>\n",
       "    <th>0.971178</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>130</th>\n",
       "    <th>0.418009</th>\n",
       "    <th>0.845380</th>\n",
       "    <th>97.013260</th>\n",
       "    <th>0.919281</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>131</th>\n",
       "    <th>0.410404</th>\n",
       "    <th>1.010399</th>\n",
       "    <th>84.565224</th>\n",
       "    <th>1.005171</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>132</th>\n",
       "    <th>0.448645</th>\n",
       "    <th>1.264179</th>\n",
       "    <th>98.708351</th>\n",
       "    <th>1.123976</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>133</th>\n",
       "    <th>0.413908</th>\n",
       "    <th>0.869661</th>\n",
       "    <th>139.217270</th>\n",
       "    <th>0.932440</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>134</th>\n",
       "    <th>0.420460</th>\n",
       "    <th>0.947159</th>\n",
       "    <th>105.644623</th>\n",
       "    <th>0.973030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>135</th>\n",
       "    <th>0.408223</th>\n",
       "    <th>0.796965</th>\n",
       "    <th>82.158836</th>\n",
       "    <th>0.891908</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>136</th>\n",
       "    <th>0.394473</th>\n",
       "    <th>0.835953</th>\n",
       "    <th>95.288734</th>\n",
       "    <th>0.913854</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>137</th>\n",
       "    <th>0.406611</th>\n",
       "    <th>0.849507</th>\n",
       "    <th>94.149872</th>\n",
       "    <th>0.921625</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>138</th>\n",
       "    <th>0.425264</th>\n",
       "    <th>0.900812</th>\n",
       "    <th>93.289253</th>\n",
       "    <th>0.948786</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>139</th>\n",
       "    <th>0.400023</th>\n",
       "    <th>0.892658</th>\n",
       "    <th>86.260635</th>\n",
       "    <th>0.944589</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>140</th>\n",
       "    <th>0.398121</th>\n",
       "    <th>1.028139</th>\n",
       "    <th>91.781929</th>\n",
       "    <th>1.013703</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>141</th>\n",
       "    <th>0.394824</th>\n",
       "    <th>1.025008</th>\n",
       "    <th>84.343185</th>\n",
       "    <th>1.012194</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>142</th>\n",
       "    <th>0.399651</th>\n",
       "    <th>0.814258</th>\n",
       "    <th>111.132004</th>\n",
       "    <th>0.902073</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>143</th>\n",
       "    <th>0.404619</th>\n",
       "    <th>0.890658</th>\n",
       "    <th>87.060867</th>\n",
       "    <th>0.943483</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>144</th>\n",
       "    <th>0.385045</th>\n",
       "    <th>0.922776</th>\n",
       "    <th>72.100136</th>\n",
       "    <th>0.960007</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>145</th>\n",
       "    <th>0.429932</th>\n",
       "    <th>0.772217</th>\n",
       "    <th>131.541168</th>\n",
       "    <th>0.878563</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>146</th>\n",
       "    <th>0.398133</th>\n",
       "    <th>0.819233</th>\n",
       "    <th>84.047897</th>\n",
       "    <th>0.904864</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>147</th>\n",
       "    <th>0.387536</th>\n",
       "    <th>0.797998</th>\n",
       "    <th>89.299164</th>\n",
       "    <th>0.893178</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>148</th>\n",
       "    <th>0.405493</th>\n",
       "    <th>0.903610</th>\n",
       "    <th>107.978584</th>\n",
       "    <th>0.949929</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>149</th>\n",
       "    <th>0.381627</th>\n",
       "    <th>0.816351</th>\n",
       "    <th>99.753426</th>\n",
       "    <th>0.903346</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>150</th>\n",
       "    <th>0.388079</th>\n",
       "    <th>0.809674</th>\n",
       "    <th>93.143234</th>\n",
       "    <th>0.899640</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>151</th>\n",
       "    <th>0.391044</th>\n",
       "    <th>0.750890</th>\n",
       "    <th>116.713875</th>\n",
       "    <th>0.865884</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>152</th>\n",
       "    <th>0.379460</th>\n",
       "    <th>0.770675</th>\n",
       "    <th>77.504097</th>\n",
       "    <th>0.877845</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>153</th>\n",
       "    <th>0.362160</th>\n",
       "    <th>0.758912</th>\n",
       "    <th>94.316231</th>\n",
       "    <th>0.870944</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>154</th>\n",
       "    <th>0.375987</th>\n",
       "    <th>0.881909</th>\n",
       "    <th>115.983864</th>\n",
       "    <th>0.939070</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>155</th>\n",
       "    <th>0.366753</th>\n",
       "    <th>0.805664</th>\n",
       "    <th>98.779236</th>\n",
       "    <th>0.897316</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>156</th>\n",
       "    <th>0.384802</th>\n",
       "    <th>0.858883</th>\n",
       "    <th>112.863853</th>\n",
       "    <th>0.926681</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>157</th>\n",
       "    <th>0.377612</th>\n",
       "    <th>0.750217</th>\n",
       "    <th>113.171425</th>\n",
       "    <th>0.866102</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>158</th>\n",
       "    <th>0.393419</th>\n",
       "    <th>0.806141</th>\n",
       "    <th>83.516937</th>\n",
       "    <th>0.897701</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>159</th>\n",
       "    <th>0.425923</th>\n",
       "    <th>0.911638</th>\n",
       "    <th>142.913895</th>\n",
       "    <th>0.954731</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>160</th>\n",
       "    <th>0.402775</th>\n",
       "    <th>0.782084</th>\n",
       "    <th>91.231117</th>\n",
       "    <th>0.884255</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>161</th>\n",
       "    <th>0.378353</th>\n",
       "    <th>0.792464</th>\n",
       "    <th>73.942009</th>\n",
       "    <th>0.889809</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>162</th>\n",
       "    <th>0.359988</th>\n",
       "    <th>0.795851</th>\n",
       "    <th>91.402512</th>\n",
       "    <th>0.891850</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>163</th>\n",
       "    <th>0.406938</th>\n",
       "    <th>0.766372</th>\n",
       "    <th>106.498253</th>\n",
       "    <th>0.874943</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>164</th>\n",
       "    <th>0.374237</th>\n",
       "    <th>0.759315</th>\n",
       "    <th>74.799362</th>\n",
       "    <th>0.871235</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>165</th>\n",
       "    <th>0.350853</th>\n",
       "    <th>0.826245</th>\n",
       "    <th>76.598259</th>\n",
       "    <th>0.908713</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>166</th>\n",
       "    <th>0.361082</th>\n",
       "    <th>0.791901</th>\n",
       "    <th>86.885361</th>\n",
       "    <th>0.889675</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>167</th>\n",
       "    <th>0.380390</th>\n",
       "    <th>0.906597</th>\n",
       "    <th>111.837212</th>\n",
       "    <th>0.951838</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>168</th>\n",
       "    <th>0.367490</th>\n",
       "    <th>0.868620</th>\n",
       "    <th>83.970474</th>\n",
       "    <th>0.931216</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>169</th>\n",
       "    <th>0.422229</th>\n",
       "    <th>0.772067</th>\n",
       "    <th>89.151100</th>\n",
       "    <th>0.878433</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>170</th>\n",
       "    <th>0.380213</th>\n",
       "    <th>0.835672</th>\n",
       "    <th>77.550385</th>\n",
       "    <th>0.913889</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>171</th>\n",
       "    <th>0.351024</th>\n",
       "    <th>0.798530</th>\n",
       "    <th>70.564293</th>\n",
       "    <th>0.893419</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>172</th>\n",
       "    <th>0.337052</th>\n",
       "    <th>0.747141</th>\n",
       "    <th>77.178871</th>\n",
       "    <th>0.863841</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>173</th>\n",
       "    <th>0.344512</th>\n",
       "    <th>0.839857</th>\n",
       "    <th>84.891747</th>\n",
       "    <th>0.915677</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>174</th>\n",
       "    <th>0.387699</th>\n",
       "    <th>0.886987</th>\n",
       "    <th>93.223549</th>\n",
       "    <th>0.941500</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>175</th>\n",
       "    <th>0.379767</th>\n",
       "    <th>0.744459</th>\n",
       "    <th>93.080574</th>\n",
       "    <th>0.862666</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>176</th>\n",
       "    <th>0.343896</th>\n",
       "    <th>0.781239</th>\n",
       "    <th>72.250282</th>\n",
       "    <th>0.883770</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>177</th>\n",
       "    <th>0.333400</th>\n",
       "    <th>0.828569</th>\n",
       "    <th>92.265587</th>\n",
       "    <th>0.909945</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>178</th>\n",
       "    <th>0.353926</th>\n",
       "    <th>0.749404</th>\n",
       "    <th>90.767807</th>\n",
       "    <th>0.865539</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>179</th>\n",
       "    <th>0.345051</th>\n",
       "    <th>0.765049</th>\n",
       "    <th>91.186295</th>\n",
       "    <th>0.874200</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>180</th>\n",
       "    <th>0.333451</th>\n",
       "    <th>0.861779</th>\n",
       "    <th>113.888496</th>\n",
       "    <th>0.928206</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>181</th>\n",
       "    <th>0.350210</th>\n",
       "    <th>0.866608</th>\n",
       "    <th>82.767464</th>\n",
       "    <th>0.930846</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>182</th>\n",
       "    <th>0.333832</th>\n",
       "    <th>0.788016</th>\n",
       "    <th>77.828529</th>\n",
       "    <th>0.887617</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>183</th>\n",
       "    <th>0.347594</th>\n",
       "    <th>0.914941</th>\n",
       "    <th>107.235359</th>\n",
       "    <th>0.956304</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>184</th>\n",
       "    <th>0.345171</th>\n",
       "    <th>0.902789</th>\n",
       "    <th>91.147713</th>\n",
       "    <th>0.950025</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>185</th>\n",
       "    <th>0.336815</th>\n",
       "    <th>0.891538</th>\n",
       "    <th>106.805000</th>\n",
       "    <th>0.943810</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>186</th>\n",
       "    <th>0.323646</th>\n",
       "    <th>0.799539</th>\n",
       "    <th>86.381248</th>\n",
       "    <th>0.894110</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>187</th>\n",
       "    <th>0.324713</th>\n",
       "    <th>0.788986</th>\n",
       "    <th>98.400391</th>\n",
       "    <th>0.888098</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>188</th>\n",
       "    <th>0.345565</th>\n",
       "    <th>0.826268</th>\n",
       "    <th>102.615425</th>\n",
       "    <th>0.908865</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>189</th>\n",
       "    <th>0.370211</th>\n",
       "    <th>0.895061</th>\n",
       "    <th>116.022087</th>\n",
       "    <th>0.945962</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>190</th>\n",
       "    <th>0.331044</th>\n",
       "    <th>0.755006</th>\n",
       "    <th>81.905067</th>\n",
       "    <th>0.868349</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>191</th>\n",
       "    <th>0.318781</th>\n",
       "    <th>0.758502</th>\n",
       "    <th>61.613487</th>\n",
       "    <th>0.870877</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>192</th>\n",
       "    <th>0.320465</th>\n",
       "    <th>0.897615</th>\n",
       "    <th>84.835007</th>\n",
       "    <th>0.947216</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>193</th>\n",
       "    <th>0.316189</th>\n",
       "    <th>0.834078</th>\n",
       "    <th>68.267654</th>\n",
       "    <th>0.912701</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>194</th>\n",
       "    <th>0.335941</th>\n",
       "    <th>0.764035</th>\n",
       "    <th>90.115723</th>\n",
       "    <th>0.873791</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>195</th>\n",
       "    <th>0.324193</th>\n",
       "    <th>0.797403</th>\n",
       "    <th>64.965546</th>\n",
       "    <th>0.892728</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>196</th>\n",
       "    <th>0.359742</th>\n",
       "    <th>1.134844</th>\n",
       "    <th>146.151077</th>\n",
       "    <th>1.065252</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>197</th>\n",
       "    <th>0.320930</th>\n",
       "    <th>0.860277</th>\n",
       "    <th>80.237473</th>\n",
       "    <th>0.926928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>198</th>\n",
       "    <th>0.322555</th>\n",
       "    <th>0.889076</th>\n",
       "    <th>88.259239</th>\n",
       "    <th>0.941705</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>199</th>\n",
       "    <th>0.304224</th>\n",
       "    <th>0.952582</th>\n",
       "    <th>84.828072</th>\n",
       "    <th>0.975697</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>200</th>\n",
       "    <th>0.326293</th>\n",
       "    <th>0.780356</th>\n",
       "    <th>90.985100</th>\n",
       "    <th>0.883298</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>201</th>\n",
       "    <th>0.302418</th>\n",
       "    <th>0.837071</th>\n",
       "    <th>82.402313</th>\n",
       "    <th>0.914552</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>202</th>\n",
       "    <th>0.320392</th>\n",
       "    <th>0.867054</th>\n",
       "    <th>120.138603</th>\n",
       "    <th>0.930783</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>203</th>\n",
       "    <th>0.317615</th>\n",
       "    <th>0.929972</th>\n",
       "    <th>69.208946</th>\n",
       "    <th>0.963967</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>204</th>\n",
       "    <th>0.312235</th>\n",
       "    <th>0.872417</th>\n",
       "    <th>92.632652</th>\n",
       "    <th>0.933760</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>205</th>\n",
       "    <th>0.296249</th>\n",
       "    <th>0.784462</th>\n",
       "    <th>92.364319</th>\n",
       "    <th>0.885592</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>206</th>\n",
       "    <th>0.306158</th>\n",
       "    <th>0.816971</th>\n",
       "    <th>94.135246</th>\n",
       "    <th>0.903701</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>207</th>\n",
       "    <th>0.306155</th>\n",
       "    <th>0.742034</th>\n",
       "    <th>63.927864</th>\n",
       "    <th>0.860868</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>208</th>\n",
       "    <th>0.301421</th>\n",
       "    <th>0.821611</th>\n",
       "    <th>82.755539</th>\n",
       "    <th>0.906163</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>209</th>\n",
       "    <th>0.320481</th>\n",
       "    <th>1.103151</th>\n",
       "    <th>122.203796</th>\n",
       "    <th>1.050255</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>210</th>\n",
       "    <th>0.301008</th>\n",
       "    <th>0.823386</th>\n",
       "    <th>89.938034</th>\n",
       "    <th>0.907202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>211</th>\n",
       "    <th>0.300694</th>\n",
       "    <th>1.203958</th>\n",
       "    <th>142.501266</th>\n",
       "    <th>1.097152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>212</th>\n",
       "    <th>0.296282</th>\n",
       "    <th>1.029641</th>\n",
       "    <th>122.960411</th>\n",
       "    <th>1.014462</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>213</th>\n",
       "    <th>0.293435</th>\n",
       "    <th>0.865260</th>\n",
       "    <th>78.518204</th>\n",
       "    <th>0.929680</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>214</th>\n",
       "    <th>0.286251</th>\n",
       "    <th>0.796625</th>\n",
       "    <th>88.672325</th>\n",
       "    <th>0.892038</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>215</th>\n",
       "    <th>0.298416</th>\n",
       "    <th>0.942071</th>\n",
       "    <th>94.338516</th>\n",
       "    <th>0.970396</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>216</th>\n",
       "    <th>0.340669</th>\n",
       "    <th>1.397497</th>\n",
       "    <th>156.087982</th>\n",
       "    <th>1.181653</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>217</th>\n",
       "    <th>0.318688</th>\n",
       "    <th>1.030097</th>\n",
       "    <th>126.973503</th>\n",
       "    <th>1.014711</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>218</th>\n",
       "    <th>0.301689</th>\n",
       "    <th>0.853487</th>\n",
       "    <th>71.159897</th>\n",
       "    <th>0.923574</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>219</th>\n",
       "    <th>0.292013</th>\n",
       "    <th>0.926609</th>\n",
       "    <th>103.604103</th>\n",
       "    <th>0.962503</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>220</th>\n",
       "    <th>0.288925</th>\n",
       "    <th>0.912864</th>\n",
       "    <th>84.617928</th>\n",
       "    <th>0.954916</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>221</th>\n",
       "    <th>0.286298</th>\n",
       "    <th>0.847233</th>\n",
       "    <th>79.584534</th>\n",
       "    <th>0.920363</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>222</th>\n",
       "    <th>0.285012</th>\n",
       "    <th>0.861566</th>\n",
       "    <th>98.047508</th>\n",
       "    <th>0.927763</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>223</th>\n",
       "    <th>0.368519</th>\n",
       "    <th>2.136118</th>\n",
       "    <th>211.229599</th>\n",
       "    <th>1.460296</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>224</th>\n",
       "    <th>0.299659</th>\n",
       "    <th>0.811044</th>\n",
       "    <th>76.671349</th>\n",
       "    <th>0.900255</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>225</th>\n",
       "    <th>0.298598</th>\n",
       "    <th>0.767647</th>\n",
       "    <th>106.283722</th>\n",
       "    <th>0.875768</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>226</th>\n",
       "    <th>0.293022</th>\n",
       "    <th>0.679274</th>\n",
       "    <th>93.893509</th>\n",
       "    <th>0.823724</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>227</th>\n",
       "    <th>0.279351</th>\n",
       "    <th>0.816693</th>\n",
       "    <th>80.401688</th>\n",
       "    <th>0.903562</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>228</th>\n",
       "    <th>0.277205</th>\n",
       "    <th>0.967702</th>\n",
       "    <th>95.106056</th>\n",
       "    <th>0.983426</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>229</th>\n",
       "    <th>0.277234</th>\n",
       "    <th>1.175310</th>\n",
       "    <th>126.180374</th>\n",
       "    <th>1.082468</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>230</th>\n",
       "    <th>0.284127</th>\n",
       "    <th>0.784944</th>\n",
       "    <th>88.475197</th>\n",
       "    <th>0.885557</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>231</th>\n",
       "    <th>0.265250</th>\n",
       "    <th>0.728695</th>\n",
       "    <th>89.943253</th>\n",
       "    <th>0.853067</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>232</th>\n",
       "    <th>0.301971</th>\n",
       "    <th>0.826045</th>\n",
       "    <th>105.790802</th>\n",
       "    <th>0.908771</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>233</th>\n",
       "    <th>0.328195</th>\n",
       "    <th>1.273563</th>\n",
       "    <th>149.128143</th>\n",
       "    <th>1.128394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>234</th>\n",
       "    <th>0.275396</th>\n",
       "    <th>0.829653</th>\n",
       "    <th>67.621216</th>\n",
       "    <th>0.910761</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>235</th>\n",
       "    <th>0.283674</th>\n",
       "    <th>0.736321</th>\n",
       "    <th>98.964249</th>\n",
       "    <th>0.857982</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>236</th>\n",
       "    <th>0.266616</th>\n",
       "    <th>0.737766</th>\n",
       "    <th>84.844200</th>\n",
       "    <th>0.858718</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>237</th>\n",
       "    <th>0.271107</th>\n",
       "    <th>0.927562</th>\n",
       "    <th>89.297714</th>\n",
       "    <th>0.962715</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>238</th>\n",
       "    <th>0.273359</th>\n",
       "    <th>0.812995</th>\n",
       "    <th>101.312759</th>\n",
       "    <th>0.901483</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>239</th>\n",
       "    <th>0.272141</th>\n",
       "    <th>0.886825</th>\n",
       "    <th>92.660278</th>\n",
       "    <th>0.941471</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>240</th>\n",
       "    <th>0.278152</th>\n",
       "    <th>0.932879</th>\n",
       "    <th>102.626686</th>\n",
       "    <th>0.965766</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>241</th>\n",
       "    <th>0.270659</th>\n",
       "    <th>0.796869</th>\n",
       "    <th>79.640648</th>\n",
       "    <th>0.892437</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>242</th>\n",
       "    <th>0.284191</th>\n",
       "    <th>0.773771</th>\n",
       "    <th>98.287148</th>\n",
       "    <th>0.879522</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>243</th>\n",
       "    <th>0.264793</th>\n",
       "    <th>0.793620</th>\n",
       "    <th>86.187035</th>\n",
       "    <th>0.890427</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>244</th>\n",
       "    <th>0.257210</th>\n",
       "    <th>0.718339</th>\n",
       "    <th>83.565300</th>\n",
       "    <th>0.847144</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>245</th>\n",
       "    <th>0.259785</th>\n",
       "    <th>0.764477</th>\n",
       "    <th>71.335388</th>\n",
       "    <th>0.874164</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>246</th>\n",
       "    <th>0.273157</th>\n",
       "    <th>0.806770</th>\n",
       "    <th>82.735222</th>\n",
       "    <th>0.897866</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>247</th>\n",
       "    <th>0.253710</th>\n",
       "    <th>0.720641</th>\n",
       "    <th>76.467751</th>\n",
       "    <th>0.848815</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>248</th>\n",
       "    <th>0.262025</th>\n",
       "    <th>0.954865</th>\n",
       "    <th>96.325684</th>\n",
       "    <th>0.976859</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>249</th>\n",
       "    <th>0.268407</th>\n",
       "    <th>0.884943</th>\n",
       "    <th>76.048065</th>\n",
       "    <th>0.940424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>250</th>\n",
       "    <th>0.298947</th>\n",
       "    <th>0.916009</th>\n",
       "    <th>133.181168</th>\n",
       "    <th>0.956657</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>251</th>\n",
       "    <th>0.318131</th>\n",
       "    <th>0.913326</th>\n",
       "    <th>108.592072</th>\n",
       "    <th>0.955611</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>252</th>\n",
       "    <th>0.296110</th>\n",
       "    <th>1.260913</th>\n",
       "    <th>167.714600</th>\n",
       "    <th>1.122329</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>253</th>\n",
       "    <th>0.276979</th>\n",
       "    <th>0.620239</th>\n",
       "    <th>113.235184</th>\n",
       "    <th>0.787310</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>254</th>\n",
       "    <th>0.277610</th>\n",
       "    <th>0.660697</th>\n",
       "    <th>86.102692</th>\n",
       "    <th>0.812336</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>255</th>\n",
       "    <th>0.291700</th>\n",
       "    <th>1.265321</th>\n",
       "    <th>134.239273</th>\n",
       "    <th>1.124314</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>256</th>\n",
       "    <th>0.280236</th>\n",
       "    <th>0.653201</th>\n",
       "    <th>86.957199</th>\n",
       "    <th>0.807950</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>257</th>\n",
       "    <th>0.272288</th>\n",
       "    <th>0.763280</th>\n",
       "    <th>114.304062</th>\n",
       "    <th>0.873640</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>258</th>\n",
       "    <th>0.249816</th>\n",
       "    <th>0.660259</th>\n",
       "    <th>75.924301</th>\n",
       "    <th>0.812333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>259</th>\n",
       "    <th>0.290930</th>\n",
       "    <th>1.174361</th>\n",
       "    <th>148.479538</th>\n",
       "    <th>1.083418</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>260</th>\n",
       "    <th>0.264436</th>\n",
       "    <th>0.746237</th>\n",
       "    <th>85.929703</th>\n",
       "    <th>0.863457</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>261</th>\n",
       "    <th>0.247780</th>\n",
       "    <th>0.741691</th>\n",
       "    <th>105.091949</th>\n",
       "    <th>0.860598</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>262</th>\n",
       "    <th>0.260235</th>\n",
       "    <th>0.693707</th>\n",
       "    <th>101.073166</th>\n",
       "    <th>0.832639</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>263</th>\n",
       "    <th>0.253443</th>\n",
       "    <th>0.763241</th>\n",
       "    <th>90.903900</th>\n",
       "    <th>0.873245</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>264</th>\n",
       "    <th>0.259091</th>\n",
       "    <th>0.807865</th>\n",
       "    <th>96.762428</th>\n",
       "    <th>0.898432</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>265</th>\n",
       "    <th>0.248276</th>\n",
       "    <th>0.838976</th>\n",
       "    <th>85.648918</th>\n",
       "    <th>0.915445</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>266</th>\n",
       "    <th>0.251074</th>\n",
       "    <th>0.751494</th>\n",
       "    <th>96.106934</th>\n",
       "    <th>0.866626</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>267</th>\n",
       "    <th>0.250623</th>\n",
       "    <th>0.757178</th>\n",
       "    <th>113.887909</th>\n",
       "    <th>0.869895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>268</th>\n",
       "    <th>0.266792</th>\n",
       "    <th>0.725149</th>\n",
       "    <th>95.338615</th>\n",
       "    <th>0.851479</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>269</th>\n",
       "    <th>0.235772</th>\n",
       "    <th>0.632280</th>\n",
       "    <th>67.180054</th>\n",
       "    <th>0.795018</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>270</th>\n",
       "    <th>0.241555</th>\n",
       "    <th>0.645678</th>\n",
       "    <th>80.581436</th>\n",
       "    <th>0.803055</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>271</th>\n",
       "    <th>0.243842</th>\n",
       "    <th>0.855396</th>\n",
       "    <th>116.358177</th>\n",
       "    <th>0.924776</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>272</th>\n",
       "    <th>0.253437</th>\n",
       "    <th>0.780699</th>\n",
       "    <th>93.213730</th>\n",
       "    <th>0.883486</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>273</th>\n",
       "    <th>0.246309</th>\n",
       "    <th>0.756766</th>\n",
       "    <th>84.243500</th>\n",
       "    <th>0.869872</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>274</th>\n",
       "    <th>0.256385</th>\n",
       "    <th>0.706212</th>\n",
       "    <th>94.465553</th>\n",
       "    <th>0.840257</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>275</th>\n",
       "    <th>0.234727</th>\n",
       "    <th>0.919730</th>\n",
       "    <th>79.999413</th>\n",
       "    <th>0.958781</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>276</th>\n",
       "    <th>0.269743</th>\n",
       "    <th>0.869879</th>\n",
       "    <th>125.565125</th>\n",
       "    <th>0.932450</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>277</th>\n",
       "    <th>0.250610</th>\n",
       "    <th>0.812189</th>\n",
       "    <th>105.235085</th>\n",
       "    <th>0.900941</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>278</th>\n",
       "    <th>0.253645</th>\n",
       "    <th>0.802904</th>\n",
       "    <th>78.564682</th>\n",
       "    <th>0.895995</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>279</th>\n",
       "    <th>0.249776</th>\n",
       "    <th>0.771783</th>\n",
       "    <th>77.184891</th>\n",
       "    <th>0.878124</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>280</th>\n",
       "    <th>0.283183</th>\n",
       "    <th>1.627154</th>\n",
       "    <th>187.646851</th>\n",
       "    <th>1.275392</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>281</th>\n",
       "    <th>0.262117</th>\n",
       "    <th>0.773728</th>\n",
       "    <th>87.275063</th>\n",
       "    <th>0.879138</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>282</th>\n",
       "    <th>0.307278</th>\n",
       "    <th>1.444251</th>\n",
       "    <th>144.313629</th>\n",
       "    <th>1.201330</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>283</th>\n",
       "    <th>0.280550</th>\n",
       "    <th>0.867839</th>\n",
       "    <th>105.944984</th>\n",
       "    <th>0.930639</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>284</th>\n",
       "    <th>0.251906</th>\n",
       "    <th>0.644228</th>\n",
       "    <th>93.046761</th>\n",
       "    <th>0.802452</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>285</th>\n",
       "    <th>0.266842</th>\n",
       "    <th>1.499736</th>\n",
       "    <th>120.020950</th>\n",
       "    <th>1.224316</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>286</th>\n",
       "    <th>0.248090</th>\n",
       "    <th>0.703440</th>\n",
       "    <th>76.797653</th>\n",
       "    <th>0.837846</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>287</th>\n",
       "    <th>0.245094</th>\n",
       "    <th>0.727104</th>\n",
       "    <th>102.375877</th>\n",
       "    <th>0.852635</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>288</th>\n",
       "    <th>0.230488</th>\n",
       "    <th>0.736103</th>\n",
       "    <th>70.846344</th>\n",
       "    <th>0.857377</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>289</th>\n",
       "    <th>0.234458</th>\n",
       "    <th>0.716166</th>\n",
       "    <th>83.603340</th>\n",
       "    <th>0.845856</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>290</th>\n",
       "    <th>0.246291</th>\n",
       "    <th>0.764694</th>\n",
       "    <th>84.420151</th>\n",
       "    <th>0.874256</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>291</th>\n",
       "    <th>0.230799</th>\n",
       "    <th>0.669530</th>\n",
       "    <th>82.840904</th>\n",
       "    <th>0.818094</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>292</th>\n",
       "    <th>0.222857</th>\n",
       "    <th>0.655805</th>\n",
       "    <th>102.233360</th>\n",
       "    <th>0.809741</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>293</th>\n",
       "    <th>0.234974</th>\n",
       "    <th>0.802511</th>\n",
       "    <th>78.100540</th>\n",
       "    <th>0.895122</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>294</th>\n",
       "    <th>0.229592</th>\n",
       "    <th>0.634351</th>\n",
       "    <th>71.844833</th>\n",
       "    <th>0.796203</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>295</th>\n",
       "    <th>0.228255</th>\n",
       "    <th>0.634107</th>\n",
       "    <th>63.998569</th>\n",
       "    <th>0.795928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>296</th>\n",
       "    <th>0.223907</th>\n",
       "    <th>0.688829</th>\n",
       "    <th>98.157127</th>\n",
       "    <th>0.829839</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>297</th>\n",
       "    <th>0.231711</th>\n",
       "    <th>0.575030</th>\n",
       "    <th>78.714806</th>\n",
       "    <th>0.757879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>298</th>\n",
       "    <th>0.245472</th>\n",
       "    <th>0.881846</th>\n",
       "    <th>70.973351</th>\n",
       "    <th>0.939009</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>299</th>\n",
       "    <th>0.228500</th>\n",
       "    <th>0.788420</th>\n",
       "    <th>69.483078</th>\n",
       "    <th>0.887438</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>300</th>\n",
       "    <th>0.226099</th>\n",
       "    <th>0.652906</th>\n",
       "    <th>67.422112</th>\n",
       "    <th>0.807895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>301</th>\n",
       "    <th>0.233976</th>\n",
       "    <th>0.731523</th>\n",
       "    <th>87.110451</th>\n",
       "    <th>0.854743</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>302</th>\n",
       "    <th>0.227537</th>\n",
       "    <th>0.796078</th>\n",
       "    <th>85.625008</th>\n",
       "    <th>0.892038</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>303</th>\n",
       "    <th>0.228869</th>\n",
       "    <th>0.699666</th>\n",
       "    <th>68.950256</th>\n",
       "    <th>0.836060</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>304</th>\n",
       "    <th>0.235716</th>\n",
       "    <th>0.868270</th>\n",
       "    <th>128.320648</th>\n",
       "    <th>0.931443</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>305</th>\n",
       "    <th>0.229153</th>\n",
       "    <th>0.899539</th>\n",
       "    <th>124.649498</th>\n",
       "    <th>0.948303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>306</th>\n",
       "    <th>0.228675</th>\n",
       "    <th>0.833931</th>\n",
       "    <th>85.563377</th>\n",
       "    <th>0.911846</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>307</th>\n",
       "    <th>0.225483</th>\n",
       "    <th>0.905352</th>\n",
       "    <th>98.984665</th>\n",
       "    <th>0.951017</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>308</th>\n",
       "    <th>0.242498</th>\n",
       "    <th>0.697858</th>\n",
       "    <th>82.813477</th>\n",
       "    <th>0.835166</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>309</th>\n",
       "    <th>0.233908</th>\n",
       "    <th>0.846067</th>\n",
       "    <th>98.336853</th>\n",
       "    <th>0.919692</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>310</th>\n",
       "    <th>0.233159</th>\n",
       "    <th>0.784875</th>\n",
       "    <th>105.450439</th>\n",
       "    <th>0.885717</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>311</th>\n",
       "    <th>0.217862</th>\n",
       "    <th>0.628170</th>\n",
       "    <th>65.930382</th>\n",
       "    <th>0.792173</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>312</th>\n",
       "    <th>0.222727</th>\n",
       "    <th>0.678630</th>\n",
       "    <th>78.981316</th>\n",
       "    <th>0.823424</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>313</th>\n",
       "    <th>0.216725</th>\n",
       "    <th>0.808236</th>\n",
       "    <th>106.970474</th>\n",
       "    <th>0.898971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>314</th>\n",
       "    <th>0.224687</th>\n",
       "    <th>0.745103</th>\n",
       "    <th>81.101463</th>\n",
       "    <th>0.862802</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>315</th>\n",
       "    <th>0.235847</th>\n",
       "    <th>0.768987</th>\n",
       "    <th>75.749054</th>\n",
       "    <th>0.876712</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>316</th>\n",
       "    <th>0.226577</th>\n",
       "    <th>15.622602</th>\n",
       "    <th>211.217300</th>\n",
       "    <th>3.929859</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>317</th>\n",
       "    <th>0.232373</th>\n",
       "    <th>0.683162</th>\n",
       "    <th>76.555077</th>\n",
       "    <th>0.826323</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>318</th>\n",
       "    <th>0.221976</th>\n",
       "    <th>0.774872</th>\n",
       "    <th>93.447609</th>\n",
       "    <th>0.880079</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>319</th>\n",
       "    <th>0.225281</th>\n",
       "    <th>1.011397</th>\n",
       "    <th>93.219055</th>\n",
       "    <th>1.005584</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>320</th>\n",
       "    <th>0.245275</th>\n",
       "    <th>0.826487</th>\n",
       "    <th>88.257790</th>\n",
       "    <th>0.908305</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>321</th>\n",
       "    <th>0.224615</th>\n",
       "    <th>1.007878</th>\n",
       "    <th>91.460358</th>\n",
       "    <th>1.003521</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>322</th>\n",
       "    <th>0.231811</th>\n",
       "    <th>0.900651</th>\n",
       "    <th>109.444824</th>\n",
       "    <th>0.948605</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>323</th>\n",
       "    <th>0.223130</th>\n",
       "    <th>0.847339</th>\n",
       "    <th>77.097298</th>\n",
       "    <th>0.920303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>324</th>\n",
       "    <th>0.219096</th>\n",
       "    <th>0.949094</th>\n",
       "    <th>87.974739</th>\n",
       "    <th>0.974052</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>325</th>\n",
       "    <th>0.221336</th>\n",
       "    <th>0.762967</th>\n",
       "    <th>74.311172</th>\n",
       "    <th>0.872458</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>326</th>\n",
       "    <th>0.220856</th>\n",
       "    <th>0.861768</th>\n",
       "    <th>95.128334</th>\n",
       "    <th>0.928116</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>327</th>\n",
       "    <th>0.244272</th>\n",
       "    <th>0.911336</th>\n",
       "    <th>68.885872</th>\n",
       "    <th>0.952458</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>328</th>\n",
       "    <th>0.262224</th>\n",
       "    <th>1.250535</th>\n",
       "    <th>130.411423</th>\n",
       "    <th>1.117355</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>329</th>\n",
       "    <th>0.235260</th>\n",
       "    <th>0.770673</th>\n",
       "    <th>99.912712</th>\n",
       "    <th>0.877648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>330</th>\n",
       "    <th>0.228126</th>\n",
       "    <th>0.956489</th>\n",
       "    <th>109.712799</th>\n",
       "    <th>0.977811</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>331</th>\n",
       "    <th>0.232188</th>\n",
       "    <th>1.319135</th>\n",
       "    <th>136.030334</th>\n",
       "    <th>1.147599</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>332</th>\n",
       "    <th>0.247319</th>\n",
       "    <th>0.645090</th>\n",
       "    <th>76.824562</th>\n",
       "    <th>0.802792</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>333</th>\n",
       "    <th>0.244676</th>\n",
       "    <th>1.998824</th>\n",
       "    <th>163.269867</th>\n",
       "    <th>1.413252</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>334</th>\n",
       "    <th>0.228852</th>\n",
       "    <th>0.816678</th>\n",
       "    <th>87.424797</th>\n",
       "    <th>0.903571</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>335</th>\n",
       "    <th>0.238165</th>\n",
       "    <th>0.777065</th>\n",
       "    <th>81.270363</th>\n",
       "    <th>0.881305</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>336</th>\n",
       "    <th>0.240483</th>\n",
       "    <th>0.933654</th>\n",
       "    <th>110.913475</th>\n",
       "    <th>0.965763</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>337</th>\n",
       "    <th>0.226187</th>\n",
       "    <th>0.873136</th>\n",
       "    <th>105.647102</th>\n",
       "    <th>0.933365</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>338</th>\n",
       "    <th>0.234225</th>\n",
       "    <th>0.896849</th>\n",
       "    <th>110.653839</th>\n",
       "    <th>0.946418</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>339</th>\n",
       "    <th>0.227547</th>\n",
       "    <th>0.902291</th>\n",
       "    <th>98.798752</th>\n",
       "    <th>0.949272</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>340</th>\n",
       "    <th>0.228916</th>\n",
       "    <th>0.831217</th>\n",
       "    <th>83.480820</th>\n",
       "    <th>0.911668</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>341</th>\n",
       "    <th>0.222605</th>\n",
       "    <th>1.130754</th>\n",
       "    <th>123.523712</th>\n",
       "    <th>1.063292</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>342</th>\n",
       "    <th>0.219920</th>\n",
       "    <th>0.834561</th>\n",
       "    <th>116.519211</th>\n",
       "    <th>0.913157</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>343</th>\n",
       "    <th>0.225831</th>\n",
       "    <th>0.666759</th>\n",
       "    <th>80.182159</th>\n",
       "    <th>0.816261</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>344</th>\n",
       "    <th>0.218172</th>\n",
       "    <th>0.681363</th>\n",
       "    <th>95.342041</th>\n",
       "    <th>0.825384</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>345</th>\n",
       "    <th>0.220457</th>\n",
       "    <th>0.657926</th>\n",
       "    <th>70.505379</th>\n",
       "    <th>0.810817</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>346</th>\n",
       "    <th>0.230048</th>\n",
       "    <th>1.177985</th>\n",
       "    <th>130.895172</th>\n",
       "    <th>1.084875</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>347</th>\n",
       "    <th>0.223617</th>\n",
       "    <th>0.684028</th>\n",
       "    <th>92.021797</th>\n",
       "    <th>0.826309</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>348</th>\n",
       "    <th>0.222605</th>\n",
       "    <th>0.694678</th>\n",
       "    <th>97.994644</th>\n",
       "    <th>0.833326</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>349</th>\n",
       "    <th>0.210572</th>\n",
       "    <th>0.690080</th>\n",
       "    <th>73.610909</th>\n",
       "    <th>0.830484</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>350</th>\n",
       "    <th>0.218730</th>\n",
       "    <th>0.689496</th>\n",
       "    <th>99.281265</th>\n",
       "    <th>0.830043</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>351</th>\n",
       "    <th>0.213774</th>\n",
       "    <th>0.691322</th>\n",
       "    <th>69.995178</th>\n",
       "    <th>0.830887</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>352</th>\n",
       "    <th>0.210645</th>\n",
       "    <th>0.683251</th>\n",
       "    <th>84.802574</th>\n",
       "    <th>0.825961</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>353</th>\n",
       "    <th>0.210441</th>\n",
       "    <th>0.764333</th>\n",
       "    <th>88.818672</th>\n",
       "    <th>0.874056</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>354</th>\n",
       "    <th>0.214490</th>\n",
       "    <th>0.941601</th>\n",
       "    <th>79.872185</th>\n",
       "    <th>0.969998</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>355</th>\n",
       "    <th>0.213130</th>\n",
       "    <th>0.639263</th>\n",
       "    <th>69.442726</th>\n",
       "    <th>0.799124</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>356</th>\n",
       "    <th>0.216927</th>\n",
       "    <th>0.983361</th>\n",
       "    <th>115.218529</th>\n",
       "    <th>0.990951</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>357</th>\n",
       "    <th>0.202668</th>\n",
       "    <th>0.647769</th>\n",
       "    <th>64.058327</th>\n",
       "    <th>0.804598</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>358</th>\n",
       "    <th>0.203280</th>\n",
       "    <th>1.040189</th>\n",
       "    <th>119.565422</th>\n",
       "    <th>1.019660</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>359</th>\n",
       "    <th>0.212284</th>\n",
       "    <th>0.660704</th>\n",
       "    <th>74.146828</th>\n",
       "    <th>0.812656</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>360</th>\n",
       "    <th>0.211444</th>\n",
       "    <th>0.928285</th>\n",
       "    <th>112.015274</th>\n",
       "    <th>0.963301</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>361</th>\n",
       "    <th>0.206098</th>\n",
       "    <th>0.683867</th>\n",
       "    <th>72.134850</th>\n",
       "    <th>0.826672</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>362</th>\n",
       "    <th>0.216711</th>\n",
       "    <th>0.783290</th>\n",
       "    <th>82.926521</th>\n",
       "    <th>0.884910</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>363</th>\n",
       "    <th>0.203371</th>\n",
       "    <th>0.606602</th>\n",
       "    <th>58.611320</th>\n",
       "    <th>0.778778</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>364</th>\n",
       "    <th>0.202435</th>\n",
       "    <th>0.791825</th>\n",
       "    <th>104.606224</th>\n",
       "    <th>0.889602</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>365</th>\n",
       "    <th>0.213709</th>\n",
       "    <th>0.615473</th>\n",
       "    <th>66.120506</th>\n",
       "    <th>0.784027</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>366</th>\n",
       "    <th>0.213674</th>\n",
       "    <th>0.758017</th>\n",
       "    <th>76.182594</th>\n",
       "    <th>0.870440</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>367</th>\n",
       "    <th>0.199344</th>\n",
       "    <th>0.675579</th>\n",
       "    <th>69.926003</th>\n",
       "    <th>0.821750</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>368</th>\n",
       "    <th>0.192811</th>\n",
       "    <th>0.841059</th>\n",
       "    <th>70.954391</th>\n",
       "    <th>0.916882</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>369</th>\n",
       "    <th>0.208975</th>\n",
       "    <th>1.145176</th>\n",
       "    <th>117.890213</th>\n",
       "    <th>1.069797</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>370</th>\n",
       "    <th>0.200861</th>\n",
       "    <th>0.719430</th>\n",
       "    <th>86.825233</th>\n",
       "    <th>0.847988</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>371</th>\n",
       "    <th>0.195095</th>\n",
       "    <th>0.667870</th>\n",
       "    <th>64.505699</th>\n",
       "    <th>0.817129</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>372</th>\n",
       "    <th>0.206399</th>\n",
       "    <th>0.775960</th>\n",
       "    <th>75.239235</th>\n",
       "    <th>0.880646</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>373</th>\n",
       "    <th>0.218792</th>\n",
       "    <th>1.019172</th>\n",
       "    <th>124.695427</th>\n",
       "    <th>1.009088</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>374</th>\n",
       "    <th>0.204049</th>\n",
       "    <th>0.806153</th>\n",
       "    <th>96.917732</th>\n",
       "    <th>0.897609</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>375</th>\n",
       "    <th>0.199696</th>\n",
       "    <th>0.756927</th>\n",
       "    <th>95.658836</th>\n",
       "    <th>0.869496</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>376</th>\n",
       "    <th>0.200408</th>\n",
       "    <th>0.768328</th>\n",
       "    <th>88.711365</th>\n",
       "    <th>0.876287</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>377</th>\n",
       "    <th>0.200879</th>\n",
       "    <th>0.828363</th>\n",
       "    <th>74.565559</th>\n",
       "    <th>0.910024</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>378</th>\n",
       "    <th>0.208887</th>\n",
       "    <th>0.844610</th>\n",
       "    <th>85.011841</th>\n",
       "    <th>0.918878</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>379</th>\n",
       "    <th>0.214161</th>\n",
       "    <th>1.626636</th>\n",
       "    <th>107.404961</th>\n",
       "    <th>1.274368</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>380</th>\n",
       "    <th>0.200820</th>\n",
       "    <th>0.768562</th>\n",
       "    <th>78.414589</th>\n",
       "    <th>0.876293</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>381</th>\n",
       "    <th>0.199092</th>\n",
       "    <th>0.668663</th>\n",
       "    <th>57.640812</th>\n",
       "    <th>0.817478</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>382</th>\n",
       "    <th>0.203397</th>\n",
       "    <th>0.949211</th>\n",
       "    <th>106.370773</th>\n",
       "    <th>0.974119</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>383</th>\n",
       "    <th>0.197458</th>\n",
       "    <th>0.797059</th>\n",
       "    <th>71.298500</th>\n",
       "    <th>0.892024</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>384</th>\n",
       "    <th>0.198394</th>\n",
       "    <th>0.730811</th>\n",
       "    <th>81.077950</th>\n",
       "    <th>0.854727</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>385</th>\n",
       "    <th>0.193977</th>\n",
       "    <th>0.734650</th>\n",
       "    <th>78.361176</th>\n",
       "    <th>0.856895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>386</th>\n",
       "    <th>0.196510</th>\n",
       "    <th>0.828459</th>\n",
       "    <th>102.868523</th>\n",
       "    <th>0.909892</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>387</th>\n",
       "    <th>0.199615</th>\n",
       "    <th>0.838440</th>\n",
       "    <th>78.512962</th>\n",
       "    <th>0.914782</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>388</th>\n",
       "    <th>0.197338</th>\n",
       "    <th>0.837759</th>\n",
       "    <th>74.124123</th>\n",
       "    <th>0.915020</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>389</th>\n",
       "    <th>0.206669</th>\n",
       "    <th>0.671283</th>\n",
       "    <th>76.024216</th>\n",
       "    <th>0.819102</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>390</th>\n",
       "    <th>0.195138</th>\n",
       "    <th>0.791319</th>\n",
       "    <th>80.677017</th>\n",
       "    <th>0.889095</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>391</th>\n",
       "    <th>0.194463</th>\n",
       "    <th>0.701071</th>\n",
       "    <th>69.961319</th>\n",
       "    <th>0.837283</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>392</th>\n",
       "    <th>0.195832</th>\n",
       "    <th>0.782509</th>\n",
       "    <th>85.922096</th>\n",
       "    <th>0.884590</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>393</th>\n",
       "    <th>0.191890</th>\n",
       "    <th>0.960386</th>\n",
       "    <th>98.123322</th>\n",
       "    <th>0.979700</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>394</th>\n",
       "    <th>0.191554</th>\n",
       "    <th>0.725894</th>\n",
       "    <th>68.308273</th>\n",
       "    <th>0.851883</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>395</th>\n",
       "    <th>0.190876</th>\n",
       "    <th>0.778486</th>\n",
       "    <th>65.531944</th>\n",
       "    <th>0.881895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>396</th>\n",
       "    <th>0.194399</th>\n",
       "    <th>0.671020</th>\n",
       "    <th>68.840775</th>\n",
       "    <th>0.818664</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>397</th>\n",
       "    <th>0.208835</th>\n",
       "    <th>0.982793</th>\n",
       "    <th>95.806450</th>\n",
       "    <th>0.991307</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>398</th>\n",
       "    <th>0.205751</th>\n",
       "    <th>0.705977</th>\n",
       "    <th>56.438782</th>\n",
       "    <th>0.840087</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>399</th>\n",
       "    <th>0.195821</th>\n",
       "    <th>0.784622</th>\n",
       "    <th>78.864128</th>\n",
       "    <th>0.885566</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>400</th>\n",
       "    <th>0.188593</th>\n",
       "    <th>0.694300</th>\n",
       "    <th>61.621632</th>\n",
       "    <th>0.833049</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>401</th>\n",
       "    <th>0.190864</th>\n",
       "    <th>0.765340</th>\n",
       "    <th>86.861610</th>\n",
       "    <th>0.874365</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>402</th>\n",
       "    <th>0.197583</th>\n",
       "    <th>0.705862</th>\n",
       "    <th>70.316513</th>\n",
       "    <th>0.839699</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>403</th>\n",
       "    <th>0.192719</th>\n",
       "    <th>0.680899</th>\n",
       "    <th>72.477875</th>\n",
       "    <th>0.825072</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>404</th>\n",
       "    <th>0.185092</th>\n",
       "    <th>1.104765</th>\n",
       "    <th>82.147903</th>\n",
       "    <th>1.050566</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>405</th>\n",
       "    <th>0.199031</th>\n",
       "    <th>0.878106</th>\n",
       "    <th>93.645966</th>\n",
       "    <th>0.936838</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>406</th>\n",
       "    <th>0.189043</th>\n",
       "    <th>0.816121</th>\n",
       "    <th>79.173332</th>\n",
       "    <th>0.902903</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>407</th>\n",
       "    <th>0.191951</th>\n",
       "    <th>0.802793</th>\n",
       "    <th>72.359047</th>\n",
       "    <th>0.895661</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>408</th>\n",
       "    <th>0.194524</th>\n",
       "    <th>0.816260</th>\n",
       "    <th>92.267502</th>\n",
       "    <th>0.903402</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>409</th>\n",
       "    <th>0.217667</th>\n",
       "    <th>1.394414</th>\n",
       "    <th>115.777298</th>\n",
       "    <th>1.180494</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>410</th>\n",
       "    <th>0.202061</th>\n",
       "    <th>1.276918</th>\n",
       "    <th>116.471886</th>\n",
       "    <th>1.129597</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>411</th>\n",
       "    <th>0.200025</th>\n",
       "    <th>0.803632</th>\n",
       "    <th>89.230888</th>\n",
       "    <th>0.896193</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>412</th>\n",
       "    <th>0.210458</th>\n",
       "    <th>1.386700</th>\n",
       "    <th>143.310989</th>\n",
       "    <th>1.177353</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>413</th>\n",
       "    <th>0.199641</th>\n",
       "    <th>1.200805</th>\n",
       "    <th>108.483002</th>\n",
       "    <th>1.095563</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>414</th>\n",
       "    <th>0.193242</th>\n",
       "    <th>0.682016</th>\n",
       "    <th>73.321884</th>\n",
       "    <th>0.825640</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>415</th>\n",
       "    <th>0.199728</th>\n",
       "    <th>1.624444</th>\n",
       "    <th>116.561096</th>\n",
       "    <th>1.274221</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>416</th>\n",
       "    <th>0.209415</th>\n",
       "    <th>0.902961</th>\n",
       "    <th>94.905296</th>\n",
       "    <th>0.949960</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>417</th>\n",
       "    <th>0.198016</th>\n",
       "    <th>0.954730</th>\n",
       "    <th>91.028816</th>\n",
       "    <th>0.976700</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>418</th>\n",
       "    <th>0.194569</th>\n",
       "    <th>1.275772</th>\n",
       "    <th>95.473236</th>\n",
       "    <th>1.128737</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>419</th>\n",
       "    <th>0.192882</th>\n",
       "    <th>0.842065</th>\n",
       "    <th>79.687355</th>\n",
       "    <th>0.916535</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>420</th>\n",
       "    <th>0.192463</th>\n",
       "    <th>1.276895</th>\n",
       "    <th>101.503540</th>\n",
       "    <th>1.129912</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>421</th>\n",
       "    <th>0.195352</th>\n",
       "    <th>0.857146</th>\n",
       "    <th>79.774940</th>\n",
       "    <th>0.925488</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>422</th>\n",
       "    <th>0.224396</th>\n",
       "    <th>1.492076</th>\n",
       "    <th>121.577148</th>\n",
       "    <th>1.221066</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>423</th>\n",
       "    <th>0.200649</th>\n",
       "    <th>0.977463</th>\n",
       "    <th>89.666359</th>\n",
       "    <th>0.988020</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>424</th>\n",
       "    <th>0.199738</th>\n",
       "    <th>1.514442</th>\n",
       "    <th>113.748985</th>\n",
       "    <th>1.230394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>425</th>\n",
       "    <th>0.204107</th>\n",
       "    <th>1.118153</th>\n",
       "    <th>107.328751</th>\n",
       "    <th>1.057078</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>426</th>\n",
       "    <th>0.195197</th>\n",
       "    <th>0.740241</th>\n",
       "    <th>68.909721</th>\n",
       "    <th>0.859928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>427</th>\n",
       "    <th>0.200876</th>\n",
       "    <th>1.407341</th>\n",
       "    <th>158.468643</th>\n",
       "    <th>1.186089</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>428</th>\n",
       "    <th>0.197815</th>\n",
       "    <th>1.395509</th>\n",
       "    <th>87.630188</th>\n",
       "    <th>1.180571</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>429</th>\n",
       "    <th>0.203609</th>\n",
       "    <th>0.785674</th>\n",
       "    <th>86.542999</th>\n",
       "    <th>0.885877</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>430</th>\n",
       "    <th>0.200887</th>\n",
       "    <th>1.272331</th>\n",
       "    <th>91.055969</th>\n",
       "    <th>1.127928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>431</th>\n",
       "    <th>0.193998</th>\n",
       "    <th>0.963452</th>\n",
       "    <th>77.150963</th>\n",
       "    <th>0.980694</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>432</th>\n",
       "    <th>0.189163</th>\n",
       "    <th>0.969940</th>\n",
       "    <th>84.530708</th>\n",
       "    <th>0.984667</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>433</th>\n",
       "    <th>0.190898</th>\n",
       "    <th>1.807559</th>\n",
       "    <th>134.333206</th>\n",
       "    <th>1.342594</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>434</th>\n",
       "    <th>0.194543</th>\n",
       "    <th>1.338659</th>\n",
       "    <th>111.516174</th>\n",
       "    <th>1.156371</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>435</th>\n",
       "    <th>0.197728</th>\n",
       "    <th>0.953306</th>\n",
       "    <th>89.595848</th>\n",
       "    <th>0.976287</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>436</th>\n",
       "    <th>0.194299</th>\n",
       "    <th>1.428059</th>\n",
       "    <th>98.974152</th>\n",
       "    <th>1.194585</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>437</th>\n",
       "    <th>0.197614</th>\n",
       "    <th>1.083766</th>\n",
       "    <th>84.498512</th>\n",
       "    <th>1.040805</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>438</th>\n",
       "    <th>0.188209</th>\n",
       "    <th>1.430697</th>\n",
       "    <th>107.003036</th>\n",
       "    <th>1.195971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>439</th>\n",
       "    <th>0.184805</th>\n",
       "    <th>0.867055</th>\n",
       "    <th>75.694725</th>\n",
       "    <th>0.930839</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>440</th>\n",
       "    <th>0.195644</th>\n",
       "    <th>1.163394</th>\n",
       "    <th>84.388885</th>\n",
       "    <th>1.078101</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>441</th>\n",
       "    <th>0.194371</th>\n",
       "    <th>1.686524</th>\n",
       "    <th>114.293236</th>\n",
       "    <th>1.297789</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>442</th>\n",
       "    <th>0.188787</th>\n",
       "    <th>1.573894</th>\n",
       "    <th>120.879585</th>\n",
       "    <th>1.253795</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>443</th>\n",
       "    <th>0.188109</th>\n",
       "    <th>1.107702</th>\n",
       "    <th>86.374893</th>\n",
       "    <th>1.052023</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>444</th>\n",
       "    <th>0.184588</th>\n",
       "    <th>1.470064</th>\n",
       "    <th>107.167923</th>\n",
       "    <th>1.212154</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>445</th>\n",
       "    <th>0.200230</th>\n",
       "    <th>1.630721</th>\n",
       "    <th>118.229691</th>\n",
       "    <th>1.276893</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>446</th>\n",
       "    <th>0.197946</th>\n",
       "    <th>1.040828</th>\n",
       "    <th>90.391953</th>\n",
       "    <th>1.019076</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>447</th>\n",
       "    <th>0.191403</th>\n",
       "    <th>1.458320</th>\n",
       "    <th>101.082573</th>\n",
       "    <th>1.207271</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>448</th>\n",
       "    <th>0.192630</th>\n",
       "    <th>1.130447</th>\n",
       "    <th>85.335304</th>\n",
       "    <th>1.062794</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>449</th>\n",
       "    <th>0.185521</th>\n",
       "    <th>0.998909</th>\n",
       "    <th>98.818504</th>\n",
       "    <th>0.999294</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>450</th>\n",
       "    <th>0.181756</th>\n",
       "    <th>0.915229</th>\n",
       "    <th>92.091148</th>\n",
       "    <th>0.956397</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>451</th>\n",
       "    <th>0.185808</th>\n",
       "    <th>0.909141</th>\n",
       "    <th>73.085854</th>\n",
       "    <th>0.952944</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>452</th>\n",
       "    <th>0.181819</th>\n",
       "    <th>0.714018</th>\n",
       "    <th>70.079498</th>\n",
       "    <th>0.844202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>453</th>\n",
       "    <th>0.191848</th>\n",
       "    <th>1.529204</th>\n",
       "    <th>111.991264</th>\n",
       "    <th>1.235726</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>454</th>\n",
       "    <th>0.179765</th>\n",
       "    <th>0.729545</th>\n",
       "    <th>75.495102</th>\n",
       "    <th>0.853801</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>455</th>\n",
       "    <th>0.188978</th>\n",
       "    <th>1.748733</th>\n",
       "    <th>102.679642</th>\n",
       "    <th>1.321902</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>456</th>\n",
       "    <th>0.185963</th>\n",
       "    <th>1.268744</th>\n",
       "    <th>86.292648</th>\n",
       "    <th>1.125784</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>457</th>\n",
       "    <th>0.187214</th>\n",
       "    <th>1.352678</th>\n",
       "    <th>107.565971</th>\n",
       "    <th>1.161716</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>458</th>\n",
       "    <th>0.184912</th>\n",
       "    <th>1.347936</th>\n",
       "    <th>86.267044</th>\n",
       "    <th>1.160743</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>459</th>\n",
       "    <th>0.181287</th>\n",
       "    <th>1.348074</th>\n",
       "    <th>100.179665</th>\n",
       "    <th>1.160834</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>460</th>\n",
       "    <th>0.184653</th>\n",
       "    <th>1.312083</th>\n",
       "    <th>97.856133</th>\n",
       "    <th>1.144514</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>461</th>\n",
       "    <th>0.182777</th>\n",
       "    <th>1.199746</th>\n",
       "    <th>95.485046</th>\n",
       "    <th>1.094271</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>462</th>\n",
       "    <th>0.196325</th>\n",
       "    <th>2.440196</th>\n",
       "    <th>147.396469</th>\n",
       "    <th>1.561132</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>463</th>\n",
       "    <th>0.190025</th>\n",
       "    <th>1.292557</th>\n",
       "    <th>99.104477</th>\n",
       "    <th>1.135500</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>464</th>\n",
       "    <th>0.181765</th>\n",
       "    <th>1.608309</th>\n",
       "    <th>99.724480</th>\n",
       "    <th>1.267494</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>465</th>\n",
       "    <th>0.182783</th>\n",
       "    <th>1.230560</th>\n",
       "    <th>112.480904</th>\n",
       "    <th>1.109152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>466</th>\n",
       "    <th>0.184785</th>\n",
       "    <th>1.616660</th>\n",
       "    <th>117.705177</th>\n",
       "    <th>1.270670</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>467</th>\n",
       "    <th>0.184853</th>\n",
       "    <th>0.897237</th>\n",
       "    <th>83.536575</th>\n",
       "    <th>0.947084</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>468</th>\n",
       "    <th>0.189486</th>\n",
       "    <th>1.945063</th>\n",
       "    <th>129.685318</th>\n",
       "    <th>1.394501</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>469</th>\n",
       "    <th>0.177805</th>\n",
       "    <th>1.565519</th>\n",
       "    <th>90.409340</th>\n",
       "    <th>1.251149</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>470</th>\n",
       "    <th>0.182446</th>\n",
       "    <th>1.238222</th>\n",
       "    <th>80.975838</th>\n",
       "    <th>1.112377</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>471</th>\n",
       "    <th>0.180425</th>\n",
       "    <th>1.063235</th>\n",
       "    <th>79.952621</th>\n",
       "    <th>1.030718</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>472</th>\n",
       "    <th>0.180596</th>\n",
       "    <th>0.968374</th>\n",
       "    <th>92.617935</th>\n",
       "    <th>0.983691</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>473</th>\n",
       "    <th>0.176510</th>\n",
       "    <th>1.846807</th>\n",
       "    <th>105.588074</th>\n",
       "    <th>1.358179</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>474</th>\n",
       "    <th>0.180593</th>\n",
       "    <th>1.381484</th>\n",
       "    <th>95.267525</th>\n",
       "    <th>1.175079</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>475</th>\n",
       "    <th>0.180575</th>\n",
       "    <th>2.565567</th>\n",
       "    <th>136.303482</th>\n",
       "    <th>1.600059</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>476</th>\n",
       "    <th>0.185613</th>\n",
       "    <th>1.132393</th>\n",
       "    <th>103.884956</th>\n",
       "    <th>1.063522</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>477</th>\n",
       "    <th>0.184088</th>\n",
       "    <th>0.989386</th>\n",
       "    <th>89.559685</th>\n",
       "    <th>0.994401</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>478</th>\n",
       "    <th>0.190501</th>\n",
       "    <th>1.138399</th>\n",
       "    <th>104.365723</th>\n",
       "    <th>1.066695</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>479</th>\n",
       "    <th>0.181047</th>\n",
       "    <th>1.452733</th>\n",
       "    <th>95.982689</th>\n",
       "    <th>1.204971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>480</th>\n",
       "    <th>0.176096</th>\n",
       "    <th>0.854483</th>\n",
       "    <th>74.355247</th>\n",
       "    <th>0.924183</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>481</th>\n",
       "    <th>0.187097</th>\n",
       "    <th>1.156760</th>\n",
       "    <th>88.985497</th>\n",
       "    <th>1.075455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>482</th>\n",
       "    <th>0.178684</th>\n",
       "    <th>0.930799</th>\n",
       "    <th>81.112732</th>\n",
       "    <th>0.964226</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>483</th>\n",
       "    <th>0.178025</th>\n",
       "    <th>1.279528</th>\n",
       "    <th>90.406868</th>\n",
       "    <th>1.131094</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>484</th>\n",
       "    <th>0.186391</th>\n",
       "    <th>1.364727</th>\n",
       "    <th>86.424973</th>\n",
       "    <th>1.167230</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>485</th>\n",
       "    <th>0.186354</th>\n",
       "    <th>0.983095</th>\n",
       "    <th>79.160904</th>\n",
       "    <th>0.991163</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>486</th>\n",
       "    <th>0.177544</th>\n",
       "    <th>1.009420</th>\n",
       "    <th>90.745972</th>\n",
       "    <th>1.003844</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>487</th>\n",
       "    <th>0.184889</th>\n",
       "    <th>1.814551</th>\n",
       "    <th>125.995125</th>\n",
       "    <th>1.346287</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>488</th>\n",
       "    <th>0.179484</th>\n",
       "    <th>0.764305</th>\n",
       "    <th>78.602089</th>\n",
       "    <th>0.873705</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>489</th>\n",
       "    <th>0.186055</th>\n",
       "    <th>1.748083</th>\n",
       "    <th>122.892578</th>\n",
       "    <th>1.321756</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>490</th>\n",
       "    <th>0.175396</th>\n",
       "    <th>1.074527</th>\n",
       "    <th>96.704437</th>\n",
       "    <th>1.036475</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>491</th>\n",
       "    <th>0.175129</th>\n",
       "    <th>0.938322</th>\n",
       "    <th>88.498825</th>\n",
       "    <th>0.968572</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>492</th>\n",
       "    <th>0.175310</th>\n",
       "    <th>1.354279</th>\n",
       "    <th>92.387299</th>\n",
       "    <th>1.162028</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>493</th>\n",
       "    <th>0.185549</th>\n",
       "    <th>1.054936</th>\n",
       "    <th>106.970879</th>\n",
       "    <th>1.026830</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>494</th>\n",
       "    <th>0.182745</th>\n",
       "    <th>1.450891</th>\n",
       "    <th>83.892860</th>\n",
       "    <th>1.204098</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>495</th>\n",
       "    <th>0.178963</th>\n",
       "    <th>1.367628</th>\n",
       "    <th>90.692406</th>\n",
       "    <th>1.169233</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>496</th>\n",
       "    <th>0.173149</th>\n",
       "    <th>1.356334</th>\n",
       "    <th>103.236328</th>\n",
       "    <th>1.164285</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>497</th>\n",
       "    <th>0.173450</th>\n",
       "    <th>1.060094</th>\n",
       "    <th>82.456886</th>\n",
       "    <th>1.029514</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>498</th>\n",
       "    <th>0.174219</th>\n",
       "    <th>1.459061</th>\n",
       "    <th>100.449722</th>\n",
       "    <th>1.207687</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>499</th>\n",
       "    <th>0.173704</th>\n",
       "    <th>1.157011</th>\n",
       "    <th>75.191353</th>\n",
       "    <th>1.074435</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>500</th>\n",
       "    <th>0.174038</th>\n",
       "    <th>1.495481</th>\n",
       "    <th>119.946777</th>\n",
       "    <th>1.222289</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>501</th>\n",
       "    <th>0.186892</th>\n",
       "    <th>1.546335</th>\n",
       "    <th>92.522652</th>\n",
       "    <th>1.242902</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>502</th>\n",
       "    <th>0.180408</th>\n",
       "    <th>0.992134</th>\n",
       "    <th>73.817253</th>\n",
       "    <th>0.995926</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>503</th>\n",
       "    <th>0.186944</th>\n",
       "    <th>1.204891</th>\n",
       "    <th>94.750137</th>\n",
       "    <th>1.097338</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>504</th>\n",
       "    <th>0.177899</th>\n",
       "    <th>0.879116</th>\n",
       "    <th>82.517441</th>\n",
       "    <th>0.937342</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>505</th>\n",
       "    <th>0.174321</th>\n",
       "    <th>1.393220</th>\n",
       "    <th>99.108917</th>\n",
       "    <th>1.180024</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>506</th>\n",
       "    <th>0.180834</th>\n",
       "    <th>3.222656</th>\n",
       "    <th>175.369171</th>\n",
       "    <th>1.794385</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>507</th>\n",
       "    <th>0.181181</th>\n",
       "    <th>0.709289</th>\n",
       "    <th>74.663620</th>\n",
       "    <th>0.841971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>508</th>\n",
       "    <th>0.177436</th>\n",
       "    <th>0.728459</th>\n",
       "    <th>78.443382</th>\n",
       "    <th>0.853275</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>509</th>\n",
       "    <th>0.182386</th>\n",
       "    <th>1.698713</th>\n",
       "    <th>119.007912</th>\n",
       "    <th>1.302674</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>510</th>\n",
       "    <th>0.172020</th>\n",
       "    <th>0.982239</th>\n",
       "    <th>97.713974</th>\n",
       "    <th>0.990470</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>511</th>\n",
       "    <th>0.174497</th>\n",
       "    <th>1.064309</th>\n",
       "    <th>101.095932</th>\n",
       "    <th>1.031364</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>512</th>\n",
       "    <th>0.175292</th>\n",
       "    <th>1.445302</th>\n",
       "    <th>128.433716</th>\n",
       "    <th>1.201857</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>513</th>\n",
       "    <th>0.180888</th>\n",
       "    <th>1.423756</th>\n",
       "    <th>108.644096</th>\n",
       "    <th>1.192465</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>514</th>\n",
       "    <th>0.180158</th>\n",
       "    <th>1.343325</th>\n",
       "    <th>103.350304</th>\n",
       "    <th>1.158688</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>515</th>\n",
       "    <th>0.186335</th>\n",
       "    <th>1.338025</th>\n",
       "    <th>121.494598</th>\n",
       "    <th>1.156510</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>516</th>\n",
       "    <th>0.175300</th>\n",
       "    <th>0.819619</th>\n",
       "    <th>79.574135</th>\n",
       "    <th>0.905202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>517</th>\n",
       "    <th>0.177099</th>\n",
       "    <th>1.356266</th>\n",
       "    <th>117.208115</th>\n",
       "    <th>1.163777</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>518</th>\n",
       "    <th>0.183087</th>\n",
       "    <th>0.958503</th>\n",
       "    <th>102.014236</th>\n",
       "    <th>0.978628</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>519</th>\n",
       "    <th>0.181763</th>\n",
       "    <th>0.772579</th>\n",
       "    <th>77.895515</th>\n",
       "    <th>0.878347</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>520</th>\n",
       "    <th>0.177078</th>\n",
       "    <th>1.123198</th>\n",
       "    <th>101.700752</th>\n",
       "    <th>1.059464</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>521</th>\n",
       "    <th>0.186109</th>\n",
       "    <th>1.422620</th>\n",
       "    <th>95.302391</th>\n",
       "    <th>1.192315</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>522</th>\n",
       "    <th>0.175709</th>\n",
       "    <th>1.085071</th>\n",
       "    <th>76.309303</th>\n",
       "    <th>1.041582</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>523</th>\n",
       "    <th>0.173821</th>\n",
       "    <th>1.046983</th>\n",
       "    <th>92.093567</th>\n",
       "    <th>1.022593</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>524</th>\n",
       "    <th>0.171753</th>\n",
       "    <th>1.341048</th>\n",
       "    <th>95.993599</th>\n",
       "    <th>1.157028</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>525</th>\n",
       "    <th>0.178343</th>\n",
       "    <th>0.963177</th>\n",
       "    <th>89.293823</th>\n",
       "    <th>0.981205</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>526</th>\n",
       "    <th>0.173930</th>\n",
       "    <th>1.298919</th>\n",
       "    <th>83.055031</th>\n",
       "    <th>1.138601</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>527</th>\n",
       "    <th>0.169903</th>\n",
       "    <th>1.398625</th>\n",
       "    <th>92.041092</th>\n",
       "    <th>1.182487</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>528</th>\n",
       "    <th>0.177049</th>\n",
       "    <th>1.458302</th>\n",
       "    <th>89.575829</th>\n",
       "    <th>1.207333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>529</th>\n",
       "    <th>0.174228</th>\n",
       "    <th>0.827934</th>\n",
       "    <th>73.972015</th>\n",
       "    <th>0.909607</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>530</th>\n",
       "    <th>0.184908</th>\n",
       "    <th>1.851847</th>\n",
       "    <th>112.942062</th>\n",
       "    <th>1.360368</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>531</th>\n",
       "    <th>0.182411</th>\n",
       "    <th>1.187760</th>\n",
       "    <th>84.653076</th>\n",
       "    <th>1.089554</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>532</th>\n",
       "    <th>0.175818</th>\n",
       "    <th>1.081256</th>\n",
       "    <th>84.001236</th>\n",
       "    <th>1.039407</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>533</th>\n",
       "    <th>0.175517</th>\n",
       "    <th>1.181890</th>\n",
       "    <th>97.405594</th>\n",
       "    <th>1.086920</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>534</th>\n",
       "    <th>0.169081</th>\n",
       "    <th>0.891171</th>\n",
       "    <th>85.892960</th>\n",
       "    <th>0.943273</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>535</th>\n",
       "    <th>0.171386</th>\n",
       "    <th>0.863105</th>\n",
       "    <th>87.835976</th>\n",
       "    <th>0.928972</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>536</th>\n",
       "    <th>0.175707</th>\n",
       "    <th>1.639969</th>\n",
       "    <th>110.209938</th>\n",
       "    <th>1.279721</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>537</th>\n",
       "    <th>0.173615</th>\n",
       "    <th>0.985973</th>\n",
       "    <th>91.514740</th>\n",
       "    <th>0.992791</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>538</th>\n",
       "    <th>0.169196</th>\n",
       "    <th>0.692376</th>\n",
       "    <th>77.770988</th>\n",
       "    <th>0.831688</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>539</th>\n",
       "    <th>0.163958</th>\n",
       "    <th>1.248190</th>\n",
       "    <th>110.881721</th>\n",
       "    <th>1.117083</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>540</th>\n",
       "    <th>0.167067</th>\n",
       "    <th>1.100993</th>\n",
       "    <th>111.580688</th>\n",
       "    <th>1.049179</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>541</th>\n",
       "    <th>0.175126</th>\n",
       "    <th>1.381462</th>\n",
       "    <th>99.215752</th>\n",
       "    <th>1.174238</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>542</th>\n",
       "    <th>0.172896</th>\n",
       "    <th>1.115579</th>\n",
       "    <th>86.352173</th>\n",
       "    <th>1.055823</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>543</th>\n",
       "    <th>0.167458</th>\n",
       "    <th>0.853393</th>\n",
       "    <th>85.671524</th>\n",
       "    <th>0.923698</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>544</th>\n",
       "    <th>0.162702</th>\n",
       "    <th>0.788280</th>\n",
       "    <th>76.614578</th>\n",
       "    <th>0.887406</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>545</th>\n",
       "    <th>0.170040</th>\n",
       "    <th>1.239248</th>\n",
       "    <th>111.969002</th>\n",
       "    <th>1.113158</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>546</th>\n",
       "    <th>0.173360</th>\n",
       "    <th>0.881492</th>\n",
       "    <th>79.143166</th>\n",
       "    <th>0.938393</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>547</th>\n",
       "    <th>0.174084</th>\n",
       "    <th>1.245871</th>\n",
       "    <th>116.359512</th>\n",
       "    <th>1.116107</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>548</th>\n",
       "    <th>0.167671</th>\n",
       "    <th>0.804089</th>\n",
       "    <th>70.485222</th>\n",
       "    <th>0.896685</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>549</th>\n",
       "    <th>0.161612</th>\n",
       "    <th>0.945899</th>\n",
       "    <th>85.004272</th>\n",
       "    <th>0.971976</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>550</th>\n",
       "    <th>0.170401</th>\n",
       "    <th>1.189675</th>\n",
       "    <th>102.994141</th>\n",
       "    <th>1.090028</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>551</th>\n",
       "    <th>0.164247</th>\n",
       "    <th>0.787654</th>\n",
       "    <th>80.033043</th>\n",
       "    <th>0.887181</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>552</th>\n",
       "    <th>0.166723</th>\n",
       "    <th>1.239319</th>\n",
       "    <th>118.600227</th>\n",
       "    <th>1.113147</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>553</th>\n",
       "    <th>0.162995</th>\n",
       "    <th>0.980513</th>\n",
       "    <th>81.865952</th>\n",
       "    <th>0.989874</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>554</th>\n",
       "    <th>0.161717</th>\n",
       "    <th>1.015110</th>\n",
       "    <th>84.519547</th>\n",
       "    <th>1.007490</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>555</th>\n",
       "    <th>0.162557</th>\n",
       "    <th>0.645522</th>\n",
       "    <th>66.945801</th>\n",
       "    <th>0.803352</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>556</th>\n",
       "    <th>0.166643</th>\n",
       "    <th>0.777428</th>\n",
       "    <th>80.095894</th>\n",
       "    <th>0.881206</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>557</th>\n",
       "    <th>0.164953</th>\n",
       "    <th>0.828415</th>\n",
       "    <th>80.857529</th>\n",
       "    <th>0.909772</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>558</th>\n",
       "    <th>0.173488</th>\n",
       "    <th>0.760314</th>\n",
       "    <th>65.886589</th>\n",
       "    <th>0.871913</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>559</th>\n",
       "    <th>0.165651</th>\n",
       "    <th>0.928753</th>\n",
       "    <th>84.067307</th>\n",
       "    <th>0.963160</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>560</th>\n",
       "    <th>0.161421</th>\n",
       "    <th>0.871313</th>\n",
       "    <th>75.573616</th>\n",
       "    <th>0.932902</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>561</th>\n",
       "    <th>0.165039</th>\n",
       "    <th>1.097705</th>\n",
       "    <th>103.571365</th>\n",
       "    <th>1.047165</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>562</th>\n",
       "    <th>0.169008</th>\n",
       "    <th>0.957982</th>\n",
       "    <th>77.245499</th>\n",
       "    <th>0.978638</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>563</th>\n",
       "    <th>0.169006</th>\n",
       "    <th>0.910113</th>\n",
       "    <th>80.223503</th>\n",
       "    <th>0.953827</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>564</th>\n",
       "    <th>0.176940</th>\n",
       "    <th>1.085906</th>\n",
       "    <th>103.012650</th>\n",
       "    <th>1.041720</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>565</th>\n",
       "    <th>0.174813</th>\n",
       "    <th>1.568586</th>\n",
       "    <th>132.739014</th>\n",
       "    <th>1.251463</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>566</th>\n",
       "    <th>0.164756</th>\n",
       "    <th>0.920890</th>\n",
       "    <th>90.711456</th>\n",
       "    <th>0.959602</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>567</th>\n",
       "    <th>0.160809</th>\n",
       "    <th>0.762684</th>\n",
       "    <th>83.335403</th>\n",
       "    <th>0.872966</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>568</th>\n",
       "    <th>0.165084</th>\n",
       "    <th>1.107860</th>\n",
       "    <th>98.265869</th>\n",
       "    <th>1.052227</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>569</th>\n",
       "    <th>0.159698</th>\n",
       "    <th>0.876365</th>\n",
       "    <th>97.461403</th>\n",
       "    <th>0.936102</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>570</th>\n",
       "    <th>0.165219</th>\n",
       "    <th>0.849489</th>\n",
       "    <th>87.569984</th>\n",
       "    <th>0.921202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>571</th>\n",
       "    <th>0.169177</th>\n",
       "    <th>0.902228</th>\n",
       "    <th>95.469139</th>\n",
       "    <th>0.948554</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>572</th>\n",
       "    <th>0.167667</th>\n",
       "    <th>0.888402</th>\n",
       "    <th>87.948723</th>\n",
       "    <th>0.941447</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>573</th>\n",
       "    <th>0.165650</th>\n",
       "    <th>0.675177</th>\n",
       "    <th>71.734947</th>\n",
       "    <th>0.821290</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>574</th>\n",
       "    <th>0.165729</th>\n",
       "    <th>1.149327</th>\n",
       "    <th>102.096809</th>\n",
       "    <th>1.071619</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>575</th>\n",
       "    <th>0.159606</th>\n",
       "    <th>0.886336</th>\n",
       "    <th>92.920746</th>\n",
       "    <th>0.941272</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>576</th>\n",
       "    <th>0.167019</th>\n",
       "    <th>0.928675</th>\n",
       "    <th>82.104568</th>\n",
       "    <th>0.963229</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>577</th>\n",
       "    <th>0.169842</th>\n",
       "    <th>0.851142</th>\n",
       "    <th>76.298653</th>\n",
       "    <th>0.922181</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>578</th>\n",
       "    <th>0.170249</th>\n",
       "    <th>1.209365</th>\n",
       "    <th>119.416328</th>\n",
       "    <th>1.099570</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>579</th>\n",
       "    <th>0.165767</th>\n",
       "    <th>0.881788</th>\n",
       "    <th>83.991089</th>\n",
       "    <th>0.938862</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>580</th>\n",
       "    <th>0.164429</th>\n",
       "    <th>0.884588</th>\n",
       "    <th>90.144722</th>\n",
       "    <th>0.940431</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>581</th>\n",
       "    <th>0.170302</th>\n",
       "    <th>0.820458</th>\n",
       "    <th>76.562866</th>\n",
       "    <th>0.905211</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>582</th>\n",
       "    <th>0.169314</th>\n",
       "    <th>1.344414</th>\n",
       "    <th>110.785896</th>\n",
       "    <th>1.158435</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>583</th>\n",
       "    <th>0.159306</th>\n",
       "    <th>0.615489</th>\n",
       "    <th>62.146336</th>\n",
       "    <th>0.784337</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>584</th>\n",
       "    <th>0.158697</th>\n",
       "    <th>0.694825</th>\n",
       "    <th>64.825104</th>\n",
       "    <th>0.833131</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>585</th>\n",
       "    <th>0.159020</th>\n",
       "    <th>0.736898</th>\n",
       "    <th>78.136421</th>\n",
       "    <th>0.858325</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>586</th>\n",
       "    <th>0.163488</th>\n",
       "    <th>0.663598</th>\n",
       "    <th>63.973255</th>\n",
       "    <th>0.814080</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>587</th>\n",
       "    <th>0.161267</th>\n",
       "    <th>0.637296</th>\n",
       "    <th>56.016014</th>\n",
       "    <th>0.797731</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>588</th>\n",
       "    <th>0.157175</th>\n",
       "    <th>0.771841</th>\n",
       "    <th>68.417297</th>\n",
       "    <th>0.878366</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>589</th>\n",
       "    <th>0.163511</th>\n",
       "    <th>0.813992</th>\n",
       "    <th>61.268864</th>\n",
       "    <th>0.902092</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>590</th>\n",
       "    <th>0.164548</th>\n",
       "    <th>0.875572</th>\n",
       "    <th>73.753685</th>\n",
       "    <th>0.935618</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>591</th>\n",
       "    <th>0.166559</th>\n",
       "    <th>1.298104</th>\n",
       "    <th>107.278816</th>\n",
       "    <th>1.137848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>592</th>\n",
       "    <th>0.166731</th>\n",
       "    <th>0.818294</th>\n",
       "    <th>90.592865</th>\n",
       "    <th>0.904463</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>593</th>\n",
       "    <th>0.156793</th>\n",
       "    <th>0.663728</th>\n",
       "    <th>65.990860</th>\n",
       "    <th>0.814412</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>594</th>\n",
       "    <th>0.161818</th>\n",
       "    <th>0.919696</th>\n",
       "    <th>115.022324</th>\n",
       "    <th>0.958819</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>595</th>\n",
       "    <th>0.158982</th>\n",
       "    <th>0.826449</th>\n",
       "    <th>80.383240</th>\n",
       "    <th>0.908598</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>596</th>\n",
       "    <th>0.165855</th>\n",
       "    <th>1.781349</th>\n",
       "    <th>121.685402</th>\n",
       "    <th>1.334434</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>597</th>\n",
       "    <th>0.160638</th>\n",
       "    <th>0.625215</th>\n",
       "    <th>56.052483</th>\n",
       "    <th>0.790191</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>598</th>\n",
       "    <th>0.179631</th>\n",
       "    <th>1.284225</th>\n",
       "    <th>94.172989</th>\n",
       "    <th>1.132477</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>599</th>\n",
       "    <th>0.161516</th>\n",
       "    <th>1.021843</th>\n",
       "    <th>108.914314</th>\n",
       "    <th>1.010665</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>600</th>\n",
       "    <th>0.159607</th>\n",
       "    <th>0.989508</th>\n",
       "    <th>93.361916</th>\n",
       "    <th>0.994467</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>601</th>\n",
       "    <th>0.158173</th>\n",
       "    <th>0.909078</th>\n",
       "    <th>81.680870</th>\n",
       "    <th>0.953261</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>602</th>\n",
       "    <th>0.156304</th>\n",
       "    <th>1.417018</th>\n",
       "    <th>132.042892</th>\n",
       "    <th>1.189611</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>603</th>\n",
       "    <th>0.158901</th>\n",
       "    <th>1.104166</th>\n",
       "    <th>102.469040</th>\n",
       "    <th>1.050051</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>604</th>\n",
       "    <th>0.159611</th>\n",
       "    <th>1.159341</th>\n",
       "    <th>102.258743</th>\n",
       "    <th>1.076596</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>605</th>\n",
       "    <th>0.156230</th>\n",
       "    <th>0.890880</th>\n",
       "    <th>81.038513</th>\n",
       "    <th>0.943516</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>606</th>\n",
       "    <th>0.159361</th>\n",
       "    <th>1.151249</th>\n",
       "    <th>100.354088</th>\n",
       "    <th>1.072655</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>607</th>\n",
       "    <th>0.163436</th>\n",
       "    <th>0.858488</th>\n",
       "    <th>65.402428</th>\n",
       "    <th>0.926456</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>608</th>\n",
       "    <th>0.155602</th>\n",
       "    <th>0.800095</th>\n",
       "    <th>72.227028</th>\n",
       "    <th>0.893997</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>609</th>\n",
       "    <th>0.161896</th>\n",
       "    <th>0.802588</th>\n",
       "    <th>79.137459</th>\n",
       "    <th>0.895788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>610</th>\n",
       "    <th>0.160477</th>\n",
       "    <th>0.927909</th>\n",
       "    <th>82.901131</th>\n",
       "    <th>0.962917</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>611</th>\n",
       "    <th>0.155754</th>\n",
       "    <th>0.880987</th>\n",
       "    <th>85.682076</th>\n",
       "    <th>0.938291</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>612</th>\n",
       "    <th>0.154068</th>\n",
       "    <th>0.889208</th>\n",
       "    <th>90.291481</th>\n",
       "    <th>0.942148</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>613</th>\n",
       "    <th>0.155726</th>\n",
       "    <th>0.928151</th>\n",
       "    <th>76.374710</th>\n",
       "    <th>0.962702</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>614</th>\n",
       "    <th>0.170776</th>\n",
       "    <th>1.111658</th>\n",
       "    <th>91.013016</th>\n",
       "    <th>1.053956</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>615</th>\n",
       "    <th>0.163195</th>\n",
       "    <th>0.949262</th>\n",
       "    <th>84.609047</th>\n",
       "    <th>0.974272</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>616</th>\n",
       "    <th>0.161378</th>\n",
       "    <th>1.235794</th>\n",
       "    <th>100.344872</th>\n",
       "    <th>1.110694</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>617</th>\n",
       "    <th>0.160325</th>\n",
       "    <th>0.670692</th>\n",
       "    <th>60.027176</th>\n",
       "    <th>0.818550</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>618</th>\n",
       "    <th>0.162426</th>\n",
       "    <th>0.968751</th>\n",
       "    <th>83.175400</th>\n",
       "    <th>0.983696</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>619</th>\n",
       "    <th>0.157808</th>\n",
       "    <th>0.693224</th>\n",
       "    <th>62.780613</th>\n",
       "    <th>0.832578</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>620</th>\n",
       "    <th>0.160107</th>\n",
       "    <th>1.035206</th>\n",
       "    <th>104.129547</th>\n",
       "    <th>1.016868</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>621</th>\n",
       "    <th>0.158575</th>\n",
       "    <th>0.868503</th>\n",
       "    <th>82.831894</th>\n",
       "    <th>0.931735</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>622</th>\n",
       "    <th>0.152495</th>\n",
       "    <th>1.209234</th>\n",
       "    <th>93.036079</th>\n",
       "    <th>1.099016</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>623</th>\n",
       "    <th>0.157412</th>\n",
       "    <th>1.276269</th>\n",
       "    <th>95.820847</th>\n",
       "    <th>1.129299</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>624</th>\n",
       "    <th>0.161852</th>\n",
       "    <th>0.598958</th>\n",
       "    <th>64.958000</th>\n",
       "    <th>0.773299</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>625</th>\n",
       "    <th>0.155155</th>\n",
       "    <th>0.752744</th>\n",
       "    <th>74.114578</th>\n",
       "    <th>0.867288</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>626</th>\n",
       "    <th>0.159236</th>\n",
       "    <th>0.718920</th>\n",
       "    <th>60.157246</th>\n",
       "    <th>0.847709</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>627</th>\n",
       "    <th>0.149817</th>\n",
       "    <th>0.690380</th>\n",
       "    <th>72.047791</th>\n",
       "    <th>0.830858</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>628</th>\n",
       "    <th>0.157312</th>\n",
       "    <th>0.939514</th>\n",
       "    <th>75.384186</th>\n",
       "    <th>0.969003</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>629</th>\n",
       "    <th>0.154122</th>\n",
       "    <th>0.693624</th>\n",
       "    <th>60.914936</th>\n",
       "    <th>0.832684</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>630</th>\n",
       "    <th>0.154418</th>\n",
       "    <th>0.778047</th>\n",
       "    <th>78.333038</th>\n",
       "    <th>0.882039</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>631</th>\n",
       "    <th>0.159416</th>\n",
       "    <th>0.784638</th>\n",
       "    <th>69.724823</th>\n",
       "    <th>0.885626</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>632</th>\n",
       "    <th>0.152666</th>\n",
       "    <th>0.865819</th>\n",
       "    <th>74.132729</th>\n",
       "    <th>0.929176</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>633</th>\n",
       "    <th>0.158930</th>\n",
       "    <th>1.517493</th>\n",
       "    <th>110.503998</th>\n",
       "    <th>1.231149</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>634</th>\n",
       "    <th>0.156463</th>\n",
       "    <th>1.001414</th>\n",
       "    <th>79.592262</th>\n",
       "    <th>1.000091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>635</th>\n",
       "    <th>0.153745</th>\n",
       "    <th>0.757459</th>\n",
       "    <th>69.522987</th>\n",
       "    <th>0.869902</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>636</th>\n",
       "    <th>0.172636</th>\n",
       "    <th>1.394411</th>\n",
       "    <th>134.424530</th>\n",
       "    <th>1.180526</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>637</th>\n",
       "    <th>0.155642</th>\n",
       "    <th>1.083498</th>\n",
       "    <th>87.976082</th>\n",
       "    <th>1.040447</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>638</th>\n",
       "    <th>0.148952</th>\n",
       "    <th>0.772564</th>\n",
       "    <th>74.042213</th>\n",
       "    <th>0.877905</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>639</th>\n",
       "    <th>0.153621</th>\n",
       "    <th>0.724997</th>\n",
       "    <th>72.397278</th>\n",
       "    <th>0.850996</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>640</th>\n",
       "    <th>0.156874</th>\n",
       "    <th>0.857371</th>\n",
       "    <th>77.512489</th>\n",
       "    <th>0.925332</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>641</th>\n",
       "    <th>0.148590</th>\n",
       "    <th>0.831811</th>\n",
       "    <th>80.805290</th>\n",
       "    <th>0.911329</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>642</th>\n",
       "    <th>0.160310</th>\n",
       "    <th>1.129749</th>\n",
       "    <th>123.900185</th>\n",
       "    <th>1.062321</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>643</th>\n",
       "    <th>0.151881</th>\n",
       "    <th>0.875648</th>\n",
       "    <th>89.534515</th>\n",
       "    <th>0.935546</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>644</th>\n",
       "    <th>0.151984</th>\n",
       "    <th>0.762671</th>\n",
       "    <th>72.876434</th>\n",
       "    <th>0.873206</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>645</th>\n",
       "    <th>0.151030</th>\n",
       "    <th>0.749866</th>\n",
       "    <th>82.375885</th>\n",
       "    <th>0.864668</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>646</th>\n",
       "    <th>0.149223</th>\n",
       "    <th>0.686414</th>\n",
       "    <th>59.858189</th>\n",
       "    <th>0.828228</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>647</th>\n",
       "    <th>0.150938</th>\n",
       "    <th>0.709556</th>\n",
       "    <th>73.134125</th>\n",
       "    <th>0.841987</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>648</th>\n",
       "    <th>0.155738</th>\n",
       "    <th>0.826865</th>\n",
       "    <th>80.177406</th>\n",
       "    <th>0.908886</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>649</th>\n",
       "    <th>0.150255</th>\n",
       "    <th>0.748767</th>\n",
       "    <th>71.170937</th>\n",
       "    <th>0.864898</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>650</th>\n",
       "    <th>0.150702</th>\n",
       "    <th>0.890812</th>\n",
       "    <th>92.918098</th>\n",
       "    <th>0.943538</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>651</th>\n",
       "    <th>0.150025</th>\n",
       "    <th>0.891806</th>\n",
       "    <th>83.293816</th>\n",
       "    <th>0.943636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>652</th>\n",
       "    <th>0.153230</th>\n",
       "    <th>0.919228</th>\n",
       "    <th>86.778717</th>\n",
       "    <th>0.958124</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>653</th>\n",
       "    <th>0.149904</th>\n",
       "    <th>0.790074</th>\n",
       "    <th>75.985062</th>\n",
       "    <th>0.888804</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>654</th>\n",
       "    <th>0.148300</th>\n",
       "    <th>0.975401</th>\n",
       "    <th>90.183228</th>\n",
       "    <th>0.987492</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>655</th>\n",
       "    <th>0.150710</th>\n",
       "    <th>0.892328</th>\n",
       "    <th>86.170319</th>\n",
       "    <th>0.944050</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>656</th>\n",
       "    <th>0.147735</th>\n",
       "    <th>1.062493</th>\n",
       "    <th>85.903252</th>\n",
       "    <th>1.030249</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>657</th>\n",
       "    <th>0.163327</th>\n",
       "    <th>1.079081</th>\n",
       "    <th>106.893814</th>\n",
       "    <th>1.038650</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>658</th>\n",
       "    <th>0.150316</th>\n",
       "    <th>0.849465</th>\n",
       "    <th>84.126328</th>\n",
       "    <th>0.921221</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>659</th>\n",
       "    <th>0.149487</th>\n",
       "    <th>0.789612</th>\n",
       "    <th>82.865158</th>\n",
       "    <th>0.888204</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>660</th>\n",
       "    <th>0.145025</th>\n",
       "    <th>0.803735</th>\n",
       "    <th>68.724777</th>\n",
       "    <th>0.896395</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>661</th>\n",
       "    <th>0.147066</th>\n",
       "    <th>0.824219</th>\n",
       "    <th>80.654984</th>\n",
       "    <th>0.907244</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>662</th>\n",
       "    <th>0.145528</th>\n",
       "    <th>0.850705</th>\n",
       "    <th>83.568024</th>\n",
       "    <th>0.921799</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>663</th>\n",
       "    <th>0.150325</th>\n",
       "    <th>0.844134</th>\n",
       "    <th>84.048813</th>\n",
       "    <th>0.918041</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>664</th>\n",
       "    <th>0.152695</th>\n",
       "    <th>0.753228</th>\n",
       "    <th>70.684982</th>\n",
       "    <th>0.867691</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>665</th>\n",
       "    <th>0.156302</th>\n",
       "    <th>0.945353</th>\n",
       "    <th>93.688828</th>\n",
       "    <th>0.971265</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>666</th>\n",
       "    <th>0.152846</th>\n",
       "    <th>1.016449</th>\n",
       "    <th>84.211159</th>\n",
       "    <th>1.007983</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>667</th>\n",
       "    <th>0.149545</th>\n",
       "    <th>0.829023</th>\n",
       "    <th>70.909149</th>\n",
       "    <th>0.910293</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>668</th>\n",
       "    <th>0.148552</th>\n",
       "    <th>0.790032</th>\n",
       "    <th>69.049629</th>\n",
       "    <th>0.888538</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>669</th>\n",
       "    <th>0.153924</th>\n",
       "    <th>0.869687</th>\n",
       "    <th>79.376213</th>\n",
       "    <th>0.932023</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>670</th>\n",
       "    <th>0.149585</th>\n",
       "    <th>0.813574</th>\n",
       "    <th>69.518509</th>\n",
       "    <th>0.900836</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>671</th>\n",
       "    <th>0.153031</th>\n",
       "    <th>0.942514</th>\n",
       "    <th>90.482330</th>\n",
       "    <th>0.970443</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>672</th>\n",
       "    <th>0.149872</th>\n",
       "    <th>0.787789</th>\n",
       "    <th>73.426529</th>\n",
       "    <th>0.887213</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>673</th>\n",
       "    <th>0.145407</th>\n",
       "    <th>0.830929</th>\n",
       "    <th>73.761185</th>\n",
       "    <th>0.911256</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>674</th>\n",
       "    <th>0.150055</th>\n",
       "    <th>0.875971</th>\n",
       "    <th>84.523293</th>\n",
       "    <th>0.935666</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>675</th>\n",
       "    <th>0.148884</th>\n",
       "    <th>1.132704</th>\n",
       "    <th>84.826317</th>\n",
       "    <th>1.063916</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>676</th>\n",
       "    <th>0.153802</th>\n",
       "    <th>0.704650</th>\n",
       "    <th>62.244461</th>\n",
       "    <th>0.838914</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>677</th>\n",
       "    <th>0.145866</th>\n",
       "    <th>0.857977</th>\n",
       "    <th>79.679253</th>\n",
       "    <th>0.925616</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>678</th>\n",
       "    <th>0.148143</th>\n",
       "    <th>0.844393</th>\n",
       "    <th>78.162659</th>\n",
       "    <th>0.918450</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>679</th>\n",
       "    <th>0.148465</th>\n",
       "    <th>1.039542</th>\n",
       "    <th>90.849503</th>\n",
       "    <th>1.019187</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>680</th>\n",
       "    <th>0.146271</th>\n",
       "    <th>0.732675</th>\n",
       "    <th>70.579155</th>\n",
       "    <th>0.855720</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>681</th>\n",
       "    <th>0.145160</th>\n",
       "    <th>0.949075</th>\n",
       "    <th>99.950027</th>\n",
       "    <th>0.973675</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>682</th>\n",
       "    <th>0.147077</th>\n",
       "    <th>0.831624</th>\n",
       "    <th>74.555153</th>\n",
       "    <th>0.911648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>683</th>\n",
       "    <th>0.151375</th>\n",
       "    <th>0.643900</th>\n",
       "    <th>66.422646</th>\n",
       "    <th>0.802242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>684</th>\n",
       "    <th>0.151483</th>\n",
       "    <th>0.796277</th>\n",
       "    <th>74.360428</th>\n",
       "    <th>0.891806</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>685</th>\n",
       "    <th>0.146627</th>\n",
       "    <th>0.698182</th>\n",
       "    <th>68.162216</th>\n",
       "    <th>0.835113</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>686</th>\n",
       "    <th>0.149764</th>\n",
       "    <th>0.716602</th>\n",
       "    <th>74.722153</th>\n",
       "    <th>0.846164</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>687</th>\n",
       "    <th>0.151648</th>\n",
       "    <th>0.683345</th>\n",
       "    <th>62.773636</th>\n",
       "    <th>0.826510</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>688</th>\n",
       "    <th>0.147346</th>\n",
       "    <th>0.829545</th>\n",
       "    <th>81.886635</th>\n",
       "    <th>0.910592</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>689</th>\n",
       "    <th>0.146854</th>\n",
       "    <th>0.741642</th>\n",
       "    <th>75.199654</th>\n",
       "    <th>0.860839</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>690</th>\n",
       "    <th>0.148829</th>\n",
       "    <th>0.827010</th>\n",
       "    <th>83.582703</th>\n",
       "    <th>0.909141</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>691</th>\n",
       "    <th>0.148929</th>\n",
       "    <th>0.759049</th>\n",
       "    <th>73.489609</th>\n",
       "    <th>0.870719</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>692</th>\n",
       "    <th>0.149247</th>\n",
       "    <th>0.858050</th>\n",
       "    <th>94.313614</th>\n",
       "    <th>0.926094</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>693</th>\n",
       "    <th>0.143388</th>\n",
       "    <th>0.829318</th>\n",
       "    <th>82.708641</th>\n",
       "    <th>0.910499</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>694</th>\n",
       "    <th>0.146423</th>\n",
       "    <th>0.712063</th>\n",
       "    <th>58.106533</th>\n",
       "    <th>0.843718</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>695</th>\n",
       "    <th>0.143257</th>\n",
       "    <th>0.792316</th>\n",
       "    <th>71.407303</th>\n",
       "    <th>0.889872</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>696</th>\n",
       "    <th>0.139467</th>\n",
       "    <th>0.765836</th>\n",
       "    <th>73.910751</th>\n",
       "    <th>0.874778</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>697</th>\n",
       "    <th>0.146959</th>\n",
       "    <th>0.900611</th>\n",
       "    <th>96.505646</th>\n",
       "    <th>0.948869</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>698</th>\n",
       "    <th>0.149362</th>\n",
       "    <th>0.987489</th>\n",
       "    <th>87.820984</th>\n",
       "    <th>0.993545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>699</th>\n",
       "    <th>0.141060</th>\n",
       "    <th>0.757943</th>\n",
       "    <th>72.608871</th>\n",
       "    <th>0.870046</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>700</th>\n",
       "    <th>0.139828</th>\n",
       "    <th>0.821429</th>\n",
       "    <th>80.135277</th>\n",
       "    <th>0.905901</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>701</th>\n",
       "    <th>0.140958</th>\n",
       "    <th>0.784438</th>\n",
       "    <th>81.769402</th>\n",
       "    <th>0.885268</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>702</th>\n",
       "    <th>0.142905</th>\n",
       "    <th>0.741616</th>\n",
       "    <th>84.047684</th>\n",
       "    <th>0.860914</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>703</th>\n",
       "    <th>0.142083</th>\n",
       "    <th>0.730264</th>\n",
       "    <th>65.630562</th>\n",
       "    <th>0.854375</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>704</th>\n",
       "    <th>0.140067</th>\n",
       "    <th>1.159846</th>\n",
       "    <th>96.942413</th>\n",
       "    <th>1.076026</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>705</th>\n",
       "    <th>0.145036</th>\n",
       "    <th>0.658421</th>\n",
       "    <th>70.390045</th>\n",
       "    <th>0.811147</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>706</th>\n",
       "    <th>0.141408</th>\n",
       "    <th>0.762116</th>\n",
       "    <th>77.951202</th>\n",
       "    <th>0.872885</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>707</th>\n",
       "    <th>0.145069</th>\n",
       "    <th>0.763490</th>\n",
       "    <th>71.338936</th>\n",
       "    <th>0.872823</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>708</th>\n",
       "    <th>0.150596</th>\n",
       "    <th>0.797016</th>\n",
       "    <th>76.652786</th>\n",
       "    <th>0.892649</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>709</th>\n",
       "    <th>0.142663</th>\n",
       "    <th>0.652987</th>\n",
       "    <th>70.484062</th>\n",
       "    <th>0.807737</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>710</th>\n",
       "    <th>0.143505</th>\n",
       "    <th>0.959911</th>\n",
       "    <th>96.830772</th>\n",
       "    <th>0.979024</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>711</th>\n",
       "    <th>0.140927</th>\n",
       "    <th>0.913673</th>\n",
       "    <th>89.596581</th>\n",
       "    <th>0.955090</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>712</th>\n",
       "    <th>0.143370</th>\n",
       "    <th>0.953766</th>\n",
       "    <th>94.438210</th>\n",
       "    <th>0.976516</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>713</th>\n",
       "    <th>0.146322</th>\n",
       "    <th>0.698682</th>\n",
       "    <th>73.057991</th>\n",
       "    <th>0.834927</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>714</th>\n",
       "    <th>0.142214</th>\n",
       "    <th>0.853727</th>\n",
       "    <th>78.537308</th>\n",
       "    <th>0.923277</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>715</th>\n",
       "    <th>0.140925</th>\n",
       "    <th>0.849873</th>\n",
       "    <th>85.139145</th>\n",
       "    <th>0.921650</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>716</th>\n",
       "    <th>0.151273</th>\n",
       "    <th>0.933031</th>\n",
       "    <th>90.031853</th>\n",
       "    <th>0.965529</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>717</th>\n",
       "    <th>0.148344</th>\n",
       "    <th>0.921854</th>\n",
       "    <th>88.754997</th>\n",
       "    <th>0.960020</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>718</th>\n",
       "    <th>0.148993</th>\n",
       "    <th>0.964579</th>\n",
       "    <th>90.154663</th>\n",
       "    <th>0.981587</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>719</th>\n",
       "    <th>0.149809</th>\n",
       "    <th>1.098740</th>\n",
       "    <th>91.298653</th>\n",
       "    <th>1.047737</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>720</th>\n",
       "    <th>0.145542</th>\n",
       "    <th>0.979445</th>\n",
       "    <th>88.924911</th>\n",
       "    <th>0.988291</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>721</th>\n",
       "    <th>0.141002</th>\n",
       "    <th>0.700170</th>\n",
       "    <th>69.151314</th>\n",
       "    <th>0.836650</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>722</th>\n",
       "    <th>0.145131</th>\n",
       "    <th>0.867658</th>\n",
       "    <th>85.411247</th>\n",
       "    <th>0.931320</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>723</th>\n",
       "    <th>0.146706</th>\n",
       "    <th>0.770292</th>\n",
       "    <th>65.098785</th>\n",
       "    <th>0.877275</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>724</th>\n",
       "    <th>0.145645</th>\n",
       "    <th>0.694163</th>\n",
       "    <th>65.057816</th>\n",
       "    <th>0.832691</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>725</th>\n",
       "    <th>0.147690</th>\n",
       "    <th>0.766687</th>\n",
       "    <th>78.103035</th>\n",
       "    <th>0.875549</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>726</th>\n",
       "    <th>0.143415</th>\n",
       "    <th>0.795148</th>\n",
       "    <th>74.662567</th>\n",
       "    <th>0.891419</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>727</th>\n",
       "    <th>0.143554</th>\n",
       "    <th>0.846380</th>\n",
       "    <th>87.079872</th>\n",
       "    <th>0.918749</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>728</th>\n",
       "    <th>0.146870</th>\n",
       "    <th>0.894122</th>\n",
       "    <th>86.347122</th>\n",
       "    <th>0.944690</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>729</th>\n",
       "    <th>0.147715</th>\n",
       "    <th>0.788871</th>\n",
       "    <th>75.539009</th>\n",
       "    <th>0.887916</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>730</th>\n",
       "    <th>0.147313</th>\n",
       "    <th>0.930390</th>\n",
       "    <th>83.511955</th>\n",
       "    <th>0.964119</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>731</th>\n",
       "    <th>0.140192</th>\n",
       "    <th>0.868210</th>\n",
       "    <th>82.070747</th>\n",
       "    <th>0.931601</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>732</th>\n",
       "    <th>0.142652</th>\n",
       "    <th>0.862561</th>\n",
       "    <th>80.056557</th>\n",
       "    <th>0.928380</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>733</th>\n",
       "    <th>0.144782</th>\n",
       "    <th>0.859035</th>\n",
       "    <th>80.776138</th>\n",
       "    <th>0.926671</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>734</th>\n",
       "    <th>0.141168</th>\n",
       "    <th>0.738665</th>\n",
       "    <th>70.779442</th>\n",
       "    <th>0.859357</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>735</th>\n",
       "    <th>0.139451</th>\n",
       "    <th>0.668353</th>\n",
       "    <th>68.009804</th>\n",
       "    <th>0.817030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>736</th>\n",
       "    <th>0.134980</th>\n",
       "    <th>0.966177</th>\n",
       "    <th>92.350700</th>\n",
       "    <th>0.982547</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>737</th>\n",
       "    <th>0.138741</th>\n",
       "    <th>0.830810</th>\n",
       "    <th>70.386642</th>\n",
       "    <th>0.911087</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>738</th>\n",
       "    <th>0.138229</th>\n",
       "    <th>0.753456</th>\n",
       "    <th>70.733963</th>\n",
       "    <th>0.867528</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>739</th>\n",
       "    <th>0.138013</th>\n",
       "    <th>0.833549</th>\n",
       "    <th>77.222694</th>\n",
       "    <th>0.912951</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>740</th>\n",
       "    <th>0.138158</th>\n",
       "    <th>0.732452</th>\n",
       "    <th>74.050972</th>\n",
       "    <th>0.855684</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>741</th>\n",
       "    <th>0.143803</th>\n",
       "    <th>0.673194</th>\n",
       "    <th>55.675224</th>\n",
       "    <th>0.820239</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>742</th>\n",
       "    <th>0.140522</th>\n",
       "    <th>0.788183</th>\n",
       "    <th>70.766106</th>\n",
       "    <th>0.887612</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>743</th>\n",
       "    <th>0.143405</th>\n",
       "    <th>0.691782</th>\n",
       "    <th>70.987053</th>\n",
       "    <th>0.831460</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>744</th>\n",
       "    <th>0.142258</th>\n",
       "    <th>0.895171</th>\n",
       "    <th>85.181229</th>\n",
       "    <th>0.945632</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>745</th>\n",
       "    <th>0.139458</th>\n",
       "    <th>0.717190</th>\n",
       "    <th>72.829903</th>\n",
       "    <th>0.846737</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>746</th>\n",
       "    <th>0.135828</th>\n",
       "    <th>0.772244</th>\n",
       "    <th>63.699245</th>\n",
       "    <th>0.878534</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>747</th>\n",
       "    <th>0.141145</th>\n",
       "    <th>0.736908</th>\n",
       "    <th>60.455173</th>\n",
       "    <th>0.857904</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>748</th>\n",
       "    <th>0.145057</th>\n",
       "    <th>0.909786</th>\n",
       "    <th>86.077454</th>\n",
       "    <th>0.953401</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>749</th>\n",
       "    <th>0.141895</th>\n",
       "    <th>0.730835</th>\n",
       "    <th>55.053806</th>\n",
       "    <th>0.854584</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>750</th>\n",
       "    <th>0.136927</th>\n",
       "    <th>0.842168</th>\n",
       "    <th>83.019073</th>\n",
       "    <th>0.917648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>751</th>\n",
       "    <th>0.140459</th>\n",
       "    <th>0.912307</th>\n",
       "    <th>80.909286</th>\n",
       "    <th>0.954241</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>752</th>\n",
       "    <th>0.140977</th>\n",
       "    <th>0.838125</th>\n",
       "    <th>80.370262</th>\n",
       "    <th>0.915179</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>753</th>\n",
       "    <th>0.138222</th>\n",
       "    <th>0.855136</th>\n",
       "    <th>92.377686</th>\n",
       "    <th>0.924563</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>754</th>\n",
       "    <th>0.141385</th>\n",
       "    <th>0.938021</th>\n",
       "    <th>92.143814</th>\n",
       "    <th>0.968454</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>755</th>\n",
       "    <th>0.139170</th>\n",
       "    <th>0.637330</th>\n",
       "    <th>49.196426</th>\n",
       "    <th>0.798256</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>756</th>\n",
       "    <th>0.135967</th>\n",
       "    <th>1.070291</th>\n",
       "    <th>80.122086</th>\n",
       "    <th>1.033933</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>757</th>\n",
       "    <th>0.141540</th>\n",
       "    <th>0.629437</th>\n",
       "    <th>56.767380</th>\n",
       "    <th>0.792818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>758</th>\n",
       "    <th>0.139181</th>\n",
       "    <th>0.610211</th>\n",
       "    <th>60.455395</th>\n",
       "    <th>0.780746</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>759</th>\n",
       "    <th>0.134261</th>\n",
       "    <th>0.626026</th>\n",
       "    <th>60.625820</th>\n",
       "    <th>0.791090</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>760</th>\n",
       "    <th>0.145350</th>\n",
       "    <th>0.966435</th>\n",
       "    <th>84.377327</th>\n",
       "    <th>0.982720</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>761</th>\n",
       "    <th>0.141219</th>\n",
       "    <th>0.782872</th>\n",
       "    <th>69.419189</th>\n",
       "    <th>0.884536</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>762</th>\n",
       "    <th>0.141002</th>\n",
       "    <th>0.722433</th>\n",
       "    <th>73.031761</th>\n",
       "    <th>0.849607</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>763</th>\n",
       "    <th>0.133006</th>\n",
       "    <th>0.720296</th>\n",
       "    <th>68.477715</th>\n",
       "    <th>0.848302</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>764</th>\n",
       "    <th>0.134138</th>\n",
       "    <th>0.786528</th>\n",
       "    <th>67.884903</th>\n",
       "    <th>0.886506</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>765</th>\n",
       "    <th>0.136377</th>\n",
       "    <th>0.805257</th>\n",
       "    <th>62.704475</th>\n",
       "    <th>0.896570</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>766</th>\n",
       "    <th>0.135813</th>\n",
       "    <th>0.679017</th>\n",
       "    <th>55.923164</th>\n",
       "    <th>0.823736</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>767</th>\n",
       "    <th>0.137290</th>\n",
       "    <th>0.874572</th>\n",
       "    <th>85.642273</th>\n",
       "    <th>0.934445</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>768</th>\n",
       "    <th>0.139021</th>\n",
       "    <th>0.722816</th>\n",
       "    <th>52.543537</th>\n",
       "    <th>0.849280</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>769</th>\n",
       "    <th>0.134938</th>\n",
       "    <th>0.721796</th>\n",
       "    <th>59.651924</th>\n",
       "    <th>0.849145</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>770</th>\n",
       "    <th>0.137956</th>\n",
       "    <th>0.672354</th>\n",
       "    <th>55.297367</th>\n",
       "    <th>0.819780</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>771</th>\n",
       "    <th>0.133038</th>\n",
       "    <th>0.672657</th>\n",
       "    <th>60.190861</th>\n",
       "    <th>0.820014</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>772</th>\n",
       "    <th>0.132928</th>\n",
       "    <th>0.707819</th>\n",
       "    <th>62.137955</th>\n",
       "    <th>0.840831</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>773</th>\n",
       "    <th>0.135470</th>\n",
       "    <th>0.760348</th>\n",
       "    <th>64.833504</th>\n",
       "    <th>0.871517</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>774</th>\n",
       "    <th>0.136777</th>\n",
       "    <th>0.782569</th>\n",
       "    <th>75.058495</th>\n",
       "    <th>0.884159</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>775</th>\n",
       "    <th>0.134267</th>\n",
       "    <th>0.595628</th>\n",
       "    <th>66.217697</th>\n",
       "    <th>0.771453</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>776</th>\n",
       "    <th>0.137450</th>\n",
       "    <th>0.810084</th>\n",
       "    <th>81.734398</th>\n",
       "    <th>0.900009</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>777</th>\n",
       "    <th>0.139154</th>\n",
       "    <th>0.834705</th>\n",
       "    <th>75.696297</th>\n",
       "    <th>0.913357</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>778</th>\n",
       "    <th>0.137214</th>\n",
       "    <th>0.803814</th>\n",
       "    <th>74.287003</th>\n",
       "    <th>0.896455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>779</th>\n",
       "    <th>0.136605</th>\n",
       "    <th>0.855918</th>\n",
       "    <th>76.202576</th>\n",
       "    <th>0.925074</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>780</th>\n",
       "    <th>0.132328</th>\n",
       "    <th>0.793015</th>\n",
       "    <th>73.810226</th>\n",
       "    <th>0.889253</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>781</th>\n",
       "    <th>0.136068</th>\n",
       "    <th>0.725413</th>\n",
       "    <th>63.327805</th>\n",
       "    <th>0.851334</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>782</th>\n",
       "    <th>0.134998</th>\n",
       "    <th>0.735236</th>\n",
       "    <th>79.457085</th>\n",
       "    <th>0.857234</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>783</th>\n",
       "    <th>0.137037</th>\n",
       "    <th>0.815389</th>\n",
       "    <th>68.237335</th>\n",
       "    <th>0.902677</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>784</th>\n",
       "    <th>0.141699</th>\n",
       "    <th>0.735451</th>\n",
       "    <th>61.698601</th>\n",
       "    <th>0.857267</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>785</th>\n",
       "    <th>0.143027</th>\n",
       "    <th>0.883287</th>\n",
       "    <th>84.608551</th>\n",
       "    <th>0.939326</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>786</th>\n",
       "    <th>0.138913</th>\n",
       "    <th>0.715244</th>\n",
       "    <th>66.618179</th>\n",
       "    <th>0.845338</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>787</th>\n",
       "    <th>0.137357</th>\n",
       "    <th>0.743260</th>\n",
       "    <th>68.992958</th>\n",
       "    <th>0.862037</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>788</th>\n",
       "    <th>0.134902</th>\n",
       "    <th>0.782906</th>\n",
       "    <th>77.621887</th>\n",
       "    <th>0.884476</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>789</th>\n",
       "    <th>0.132085</th>\n",
       "    <th>0.755246</th>\n",
       "    <th>67.271080</th>\n",
       "    <th>0.868662</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>790</th>\n",
       "    <th>0.136043</th>\n",
       "    <th>0.690477</th>\n",
       "    <th>71.485229</th>\n",
       "    <th>0.830748</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>791</th>\n",
       "    <th>0.130810</th>\n",
       "    <th>0.642984</th>\n",
       "    <th>56.834675</th>\n",
       "    <th>0.801487</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>792</th>\n",
       "    <th>0.133258</th>\n",
       "    <th>0.780046</th>\n",
       "    <th>75.070984</th>\n",
       "    <th>0.882963</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>793</th>\n",
       "    <th>0.134483</th>\n",
       "    <th>0.739007</th>\n",
       "    <th>68.777054</th>\n",
       "    <th>0.859553</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>794</th>\n",
       "    <th>0.132461</th>\n",
       "    <th>0.727079</th>\n",
       "    <th>69.103584</th>\n",
       "    <th>0.852639</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>795</th>\n",
       "    <th>0.132995</th>\n",
       "    <th>0.781789</th>\n",
       "    <th>70.328239</th>\n",
       "    <th>0.884096</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>796</th>\n",
       "    <th>0.137530</th>\n",
       "    <th>0.697417</th>\n",
       "    <th>56.050949</th>\n",
       "    <th>0.834785</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>797</th>\n",
       "    <th>0.138565</th>\n",
       "    <th>0.918546</th>\n",
       "    <th>90.393265</th>\n",
       "    <th>0.958260</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>798</th>\n",
       "    <th>0.142959</th>\n",
       "    <th>0.803625</th>\n",
       "    <th>65.658516</th>\n",
       "    <th>0.896293</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>799</th>\n",
       "    <th>0.134219</th>\n",
       "    <th>0.827149</th>\n",
       "    <th>82.063705</th>\n",
       "    <th>0.908917</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>800</th>\n",
       "    <th>0.135272</th>\n",
       "    <th>0.807847</th>\n",
       "    <th>82.009331</th>\n",
       "    <th>0.898407</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>801</th>\n",
       "    <th>0.137839</th>\n",
       "    <th>0.759706</th>\n",
       "    <th>75.174141</th>\n",
       "    <th>0.870845</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>802</th>\n",
       "    <th>0.133868</th>\n",
       "    <th>0.681547</th>\n",
       "    <th>62.379974</th>\n",
       "    <th>0.825324</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>803</th>\n",
       "    <th>0.133795</th>\n",
       "    <th>0.824020</th>\n",
       "    <th>84.770927</th>\n",
       "    <th>0.907205</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>804</th>\n",
       "    <th>0.132306</th>\n",
       "    <th>0.829868</th>\n",
       "    <th>71.151321</th>\n",
       "    <th>0.910714</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>805</th>\n",
       "    <th>0.132891</th>\n",
       "    <th>0.739162</th>\n",
       "    <th>70.403473</th>\n",
       "    <th>0.859366</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>806</th>\n",
       "    <th>0.140140</th>\n",
       "    <th>0.804481</th>\n",
       "    <th>82.427689</th>\n",
       "    <th>0.896211</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>807</th>\n",
       "    <th>0.135848</th>\n",
       "    <th>0.812914</th>\n",
       "    <th>74.971100</th>\n",
       "    <th>0.901411</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>808</th>\n",
       "    <th>0.136145</th>\n",
       "    <th>0.770378</th>\n",
       "    <th>76.852348</th>\n",
       "    <th>0.877685</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>809</th>\n",
       "    <th>0.132427</th>\n",
       "    <th>0.986331</th>\n",
       "    <th>105.018288</th>\n",
       "    <th>0.992893</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>810</th>\n",
       "    <th>0.131354</th>\n",
       "    <th>0.812691</th>\n",
       "    <th>81.480278</th>\n",
       "    <th>0.901344</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>811</th>\n",
       "    <th>0.129834</th>\n",
       "    <th>0.832237</th>\n",
       "    <th>71.475166</th>\n",
       "    <th>0.911851</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>812</th>\n",
       "    <th>0.131838</th>\n",
       "    <th>0.783876</th>\n",
       "    <th>77.681763</th>\n",
       "    <th>0.884866</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>813</th>\n",
       "    <th>0.131571</th>\n",
       "    <th>0.753372</th>\n",
       "    <th>74.308540</th>\n",
       "    <th>0.867688</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>814</th>\n",
       "    <th>0.133947</th>\n",
       "    <th>0.711661</th>\n",
       "    <th>72.192642</th>\n",
       "    <th>0.843313</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>815</th>\n",
       "    <th>0.132863</th>\n",
       "    <th>0.809984</th>\n",
       "    <th>80.340225</th>\n",
       "    <th>0.899666</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>816</th>\n",
       "    <th>0.127983</th>\n",
       "    <th>0.832700</th>\n",
       "    <th>80.468849</th>\n",
       "    <th>0.912282</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>817</th>\n",
       "    <th>0.135596</th>\n",
       "    <th>0.817659</th>\n",
       "    <th>67.471581</th>\n",
       "    <th>0.903779</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>818</th>\n",
       "    <th>0.127992</th>\n",
       "    <th>0.800468</th>\n",
       "    <th>72.416512</th>\n",
       "    <th>0.894554</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>819</th>\n",
       "    <th>0.135223</th>\n",
       "    <th>0.799768</th>\n",
       "    <th>75.606560</th>\n",
       "    <th>0.894012</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>820</th>\n",
       "    <th>0.130051</th>\n",
       "    <th>0.788218</th>\n",
       "    <th>70.451294</th>\n",
       "    <th>0.886550</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>821</th>\n",
       "    <th>0.132455</th>\n",
       "    <th>0.859685</th>\n",
       "    <th>82.569725</th>\n",
       "    <th>0.925971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>822</th>\n",
       "    <th>0.128277</th>\n",
       "    <th>0.930151</th>\n",
       "    <th>88.491119</th>\n",
       "    <th>0.964150</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>823</th>\n",
       "    <th>0.130052</th>\n",
       "    <th>0.738669</th>\n",
       "    <th>69.206551</th>\n",
       "    <th>0.858968</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>824</th>\n",
       "    <th>0.127728</th>\n",
       "    <th>0.685790</th>\n",
       "    <th>70.349274</th>\n",
       "    <th>0.827884</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>825</th>\n",
       "    <th>0.127141</th>\n",
       "    <th>0.973838</th>\n",
       "    <th>82.392159</th>\n",
       "    <th>0.986199</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>826</th>\n",
       "    <th>0.126370</th>\n",
       "    <th>0.671615</th>\n",
       "    <th>78.842697</th>\n",
       "    <th>0.819178</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>827</th>\n",
       "    <th>0.129487</th>\n",
       "    <th>0.712044</th>\n",
       "    <th>64.748306</th>\n",
       "    <th>0.843302</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>828</th>\n",
       "    <th>0.125942</th>\n",
       "    <th>0.681542</th>\n",
       "    <th>63.548492</th>\n",
       "    <th>0.824952</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>829</th>\n",
       "    <th>0.132295</th>\n",
       "    <th>0.710244</th>\n",
       "    <th>73.662628</th>\n",
       "    <th>0.842581</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>830</th>\n",
       "    <th>0.132181</th>\n",
       "    <th>0.685735</th>\n",
       "    <th>61.087967</th>\n",
       "    <th>0.827791</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>831</th>\n",
       "    <th>0.129376</th>\n",
       "    <th>0.732298</th>\n",
       "    <th>70.953171</th>\n",
       "    <th>0.855650</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>832</th>\n",
       "    <th>0.131665</th>\n",
       "    <th>0.655070</th>\n",
       "    <th>53.336552</th>\n",
       "    <th>0.809148</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>833</th>\n",
       "    <th>0.129135</th>\n",
       "    <th>0.767638</th>\n",
       "    <th>71.542015</th>\n",
       "    <th>0.875895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>834</th>\n",
       "    <th>0.127851</th>\n",
       "    <th>0.738866</th>\n",
       "    <th>69.451195</th>\n",
       "    <th>0.859400</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>835</th>\n",
       "    <th>0.128787</th>\n",
       "    <th>0.763613</th>\n",
       "    <th>81.898903</th>\n",
       "    <th>0.873193</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>836</th>\n",
       "    <th>0.135071</th>\n",
       "    <th>0.874869</th>\n",
       "    <th>81.566391</th>\n",
       "    <th>0.935138</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>837</th>\n",
       "    <th>0.133684</th>\n",
       "    <th>0.827637</th>\n",
       "    <th>74.299240</th>\n",
       "    <th>0.908775</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>838</th>\n",
       "    <th>0.129206</th>\n",
       "    <th>0.769226</th>\n",
       "    <th>70.697342</th>\n",
       "    <th>0.876766</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>839</th>\n",
       "    <th>0.129678</th>\n",
       "    <th>0.641792</th>\n",
       "    <th>54.961533</th>\n",
       "    <th>0.800852</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>840</th>\n",
       "    <th>0.124755</th>\n",
       "    <th>0.679752</th>\n",
       "    <th>60.352402</th>\n",
       "    <th>0.824387</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>841</th>\n",
       "    <th>0.131354</th>\n",
       "    <th>0.754859</th>\n",
       "    <th>63.659889</th>\n",
       "    <th>0.868705</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>842</th>\n",
       "    <th>0.130730</th>\n",
       "    <th>0.803999</th>\n",
       "    <th>67.010056</th>\n",
       "    <th>0.895996</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>843</th>\n",
       "    <th>0.129934</th>\n",
       "    <th>0.765631</th>\n",
       "    <th>73.773239</th>\n",
       "    <th>0.874789</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>844</th>\n",
       "    <th>0.129745</th>\n",
       "    <th>0.726068</th>\n",
       "    <th>64.114990</th>\n",
       "    <th>0.851836</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>845</th>\n",
       "    <th>0.131370</th>\n",
       "    <th>0.693417</th>\n",
       "    <th>64.278412</th>\n",
       "    <th>0.832478</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>846</th>\n",
       "    <th>0.129534</th>\n",
       "    <th>0.791103</th>\n",
       "    <th>70.968597</th>\n",
       "    <th>0.889275</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>847</th>\n",
       "    <th>0.126288</th>\n",
       "    <th>0.817280</th>\n",
       "    <th>80.383675</th>\n",
       "    <th>0.903434</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>848</th>\n",
       "    <th>0.125069</th>\n",
       "    <th>0.855840</th>\n",
       "    <th>87.548981</th>\n",
       "    <th>0.924328</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>849</th>\n",
       "    <th>0.129508</th>\n",
       "    <th>0.802894</th>\n",
       "    <th>75.846062</th>\n",
       "    <th>0.895558</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>850</th>\n",
       "    <th>0.127506</th>\n",
       "    <th>0.806673</th>\n",
       "    <th>78.925148</th>\n",
       "    <th>0.897788</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>851</th>\n",
       "    <th>0.126308</th>\n",
       "    <th>0.770416</th>\n",
       "    <th>70.535324</th>\n",
       "    <th>0.877619</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>852</th>\n",
       "    <th>0.124011</th>\n",
       "    <th>0.785654</th>\n",
       "    <th>73.624588</th>\n",
       "    <th>0.886212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>853</th>\n",
       "    <th>0.122685</th>\n",
       "    <th>0.860758</th>\n",
       "    <th>80.499603</th>\n",
       "    <th>0.927246</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>854</th>\n",
       "    <th>0.130077</th>\n",
       "    <th>0.934538</th>\n",
       "    <th>86.273361</th>\n",
       "    <th>0.965886</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>855</th>\n",
       "    <th>0.130061</th>\n",
       "    <th>0.787638</th>\n",
       "    <th>73.446999</th>\n",
       "    <th>0.887382</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>856</th>\n",
       "    <th>0.127646</th>\n",
       "    <th>0.792422</th>\n",
       "    <th>79.226295</th>\n",
       "    <th>0.889912</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>857</th>\n",
       "    <th>0.125300</th>\n",
       "    <th>0.699591</th>\n",
       "    <th>69.284447</th>\n",
       "    <th>0.835967</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>858</th>\n",
       "    <th>0.125912</th>\n",
       "    <th>0.820846</th>\n",
       "    <th>68.813934</th>\n",
       "    <th>0.905570</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>859</th>\n",
       "    <th>0.126598</th>\n",
       "    <th>0.686241</th>\n",
       "    <th>53.014824</th>\n",
       "    <th>0.827954</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>860</th>\n",
       "    <th>0.128294</th>\n",
       "    <th>0.848718</th>\n",
       "    <th>73.271309</th>\n",
       "    <th>0.921079</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>861</th>\n",
       "    <th>0.129246</th>\n",
       "    <th>0.760581</th>\n",
       "    <th>62.571987</th>\n",
       "    <th>0.871836</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>862</th>\n",
       "    <th>0.127758</th>\n",
       "    <th>0.797367</th>\n",
       "    <th>79.280708</th>\n",
       "    <th>0.892465</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>863</th>\n",
       "    <th>0.125410</th>\n",
       "    <th>0.840746</th>\n",
       "    <th>70.574196</th>\n",
       "    <th>0.916674</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>864</th>\n",
       "    <th>0.124489</th>\n",
       "    <th>0.779022</th>\n",
       "    <th>69.533325</th>\n",
       "    <th>0.882290</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>865</th>\n",
       "    <th>0.127927</th>\n",
       "    <th>0.784191</th>\n",
       "    <th>76.773239</th>\n",
       "    <th>0.885480</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>866</th>\n",
       "    <th>0.123134</th>\n",
       "    <th>0.729571</th>\n",
       "    <th>60.989555</th>\n",
       "    <th>0.853589</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>867</th>\n",
       "    <th>0.119733</th>\n",
       "    <th>0.769269</th>\n",
       "    <th>69.834816</th>\n",
       "    <th>0.876822</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>868</th>\n",
       "    <th>0.123930</th>\n",
       "    <th>0.803790</th>\n",
       "    <th>75.501091</th>\n",
       "    <th>0.896240</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>869</th>\n",
       "    <th>0.123547</th>\n",
       "    <th>0.719518</th>\n",
       "    <th>61.469032</th>\n",
       "    <th>0.848037</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>870</th>\n",
       "    <th>0.128810</th>\n",
       "    <th>0.721589</th>\n",
       "    <th>67.405113</th>\n",
       "    <th>0.849203</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>871</th>\n",
       "    <th>0.125005</th>\n",
       "    <th>0.697703</th>\n",
       "    <th>67.956528</th>\n",
       "    <th>0.835003</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>872</th>\n",
       "    <th>0.122462</th>\n",
       "    <th>0.813084</th>\n",
       "    <th>73.111679</th>\n",
       "    <th>0.901506</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>873</th>\n",
       "    <th>0.122447</th>\n",
       "    <th>0.740543</th>\n",
       "    <th>73.670097</th>\n",
       "    <th>0.860176</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>874</th>\n",
       "    <th>0.126857</th>\n",
       "    <th>0.749528</th>\n",
       "    <th>72.324921</th>\n",
       "    <th>0.865690</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>875</th>\n",
       "    <th>0.131695</th>\n",
       "    <th>0.722813</th>\n",
       "    <th>61.078850</th>\n",
       "    <th>0.849694</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>876</th>\n",
       "    <th>0.125880</th>\n",
       "    <th>0.662522</th>\n",
       "    <th>62.707043</th>\n",
       "    <th>0.813879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>877</th>\n",
       "    <th>0.126684</th>\n",
       "    <th>0.751525</th>\n",
       "    <th>70.285187</th>\n",
       "    <th>0.866694</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>878</th>\n",
       "    <th>0.124539</th>\n",
       "    <th>0.704868</th>\n",
       "    <th>59.220436</th>\n",
       "    <th>0.838918</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>879</th>\n",
       "    <th>0.124954</th>\n",
       "    <th>0.828557</th>\n",
       "    <th>72.955673</th>\n",
       "    <th>0.909943</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>880</th>\n",
       "    <th>0.123608</th>\n",
       "    <th>0.787326</th>\n",
       "    <th>71.788651</th>\n",
       "    <th>0.887207</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>881</th>\n",
       "    <th>0.127098</th>\n",
       "    <th>0.748183</th>\n",
       "    <th>60.022919</th>\n",
       "    <th>0.864737</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>882</th>\n",
       "    <th>0.126865</th>\n",
       "    <th>0.851146</th>\n",
       "    <th>87.225677</th>\n",
       "    <th>0.921999</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>883</th>\n",
       "    <th>0.125689</th>\n",
       "    <th>0.824449</th>\n",
       "    <th>82.027603</th>\n",
       "    <th>0.907264</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>884</th>\n",
       "    <th>0.124584</th>\n",
       "    <th>0.760740</th>\n",
       "    <th>68.316032</th>\n",
       "    <th>0.871976</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>885</th>\n",
       "    <th>0.120108</th>\n",
       "    <th>0.774543</th>\n",
       "    <th>61.065792</th>\n",
       "    <th>0.879950</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>886</th>\n",
       "    <th>0.125730</th>\n",
       "    <th>0.932809</th>\n",
       "    <th>85.708900</th>\n",
       "    <th>0.965033</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>887</th>\n",
       "    <th>0.124557</th>\n",
       "    <th>0.814857</th>\n",
       "    <th>73.208153</th>\n",
       "    <th>0.902515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>888</th>\n",
       "    <th>0.123117</th>\n",
       "    <th>0.705696</th>\n",
       "    <th>68.562347</th>\n",
       "    <th>0.839692</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>889</th>\n",
       "    <th>0.121852</th>\n",
       "    <th>0.721715</th>\n",
       "    <th>61.536713</th>\n",
       "    <th>0.849473</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>890</th>\n",
       "    <th>0.126098</th>\n",
       "    <th>0.742216</th>\n",
       "    <th>60.987450</th>\n",
       "    <th>0.860817</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>891</th>\n",
       "    <th>0.120653</th>\n",
       "    <th>1.062375</th>\n",
       "    <th>103.519363</th>\n",
       "    <th>1.030538</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>892</th>\n",
       "    <th>0.123961</th>\n",
       "    <th>0.790269</th>\n",
       "    <th>77.207947</th>\n",
       "    <th>0.888449</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>893</th>\n",
       "    <th>0.120068</th>\n",
       "    <th>0.719687</th>\n",
       "    <th>70.695168</th>\n",
       "    <th>0.847840</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>894</th>\n",
       "    <th>0.119714</th>\n",
       "    <th>0.791245</th>\n",
       "    <th>75.368866</th>\n",
       "    <th>0.889199</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>895</th>\n",
       "    <th>0.119573</th>\n",
       "    <th>0.818452</th>\n",
       "    <th>73.549835</th>\n",
       "    <th>0.904163</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>896</th>\n",
       "    <th>0.122320</th>\n",
       "    <th>0.808618</th>\n",
       "    <th>78.424637</th>\n",
       "    <th>0.899149</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>897</th>\n",
       "    <th>0.125887</th>\n",
       "    <th>0.824732</th>\n",
       "    <th>80.290741</th>\n",
       "    <th>0.907920</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>898</th>\n",
       "    <th>0.117799</th>\n",
       "    <th>0.836124</th>\n",
       "    <th>85.060043</th>\n",
       "    <th>0.913563</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>899</th>\n",
       "    <th>0.121143</th>\n",
       "    <th>0.758994</th>\n",
       "    <th>70.913597</th>\n",
       "    <th>0.870981</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>900</th>\n",
       "    <th>0.125233</th>\n",
       "    <th>0.873077</th>\n",
       "    <th>83.457085</th>\n",
       "    <th>0.934128</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>901</th>\n",
       "    <th>0.120287</th>\n",
       "    <th>0.849922</th>\n",
       "    <th>80.327820</th>\n",
       "    <th>0.921709</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>902</th>\n",
       "    <th>0.122525</th>\n",
       "    <th>0.783704</th>\n",
       "    <th>79.133583</th>\n",
       "    <th>0.884678</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>903</th>\n",
       "    <th>0.123662</th>\n",
       "    <th>0.756394</th>\n",
       "    <th>67.055534</th>\n",
       "    <th>0.869606</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>904</th>\n",
       "    <th>0.121717</th>\n",
       "    <th>0.865878</th>\n",
       "    <th>70.123909</th>\n",
       "    <th>0.930122</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>905</th>\n",
       "    <th>0.120713</th>\n",
       "    <th>0.793093</th>\n",
       "    <th>85.052780</th>\n",
       "    <th>0.889698</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>906</th>\n",
       "    <th>0.121080</th>\n",
       "    <th>0.889493</th>\n",
       "    <th>85.164940</th>\n",
       "    <th>0.942971</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>907</th>\n",
       "    <th>0.117612</th>\n",
       "    <th>0.844185</th>\n",
       "    <th>80.960449</th>\n",
       "    <th>0.918127</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>908</th>\n",
       "    <th>0.120263</th>\n",
       "    <th>0.838418</th>\n",
       "    <th>78.550041</th>\n",
       "    <th>0.915385</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>909</th>\n",
       "    <th>0.119573</th>\n",
       "    <th>0.782555</th>\n",
       "    <th>78.009926</th>\n",
       "    <th>0.884186</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>910</th>\n",
       "    <th>0.120953</th>\n",
       "    <th>0.863941</th>\n",
       "    <th>78.142128</th>\n",
       "    <th>0.928819</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>911</th>\n",
       "    <th>0.121647</th>\n",
       "    <th>0.796701</th>\n",
       "    <th>68.384628</th>\n",
       "    <th>0.892066</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>912</th>\n",
       "    <th>0.122894</th>\n",
       "    <th>0.756591</th>\n",
       "    <th>75.006775</th>\n",
       "    <th>0.869502</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>913</th>\n",
       "    <th>0.118885</th>\n",
       "    <th>0.750115</th>\n",
       "    <th>70.856598</th>\n",
       "    <th>0.865811</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>914</th>\n",
       "    <th>0.117796</th>\n",
       "    <th>0.668032</th>\n",
       "    <th>67.041161</th>\n",
       "    <th>0.817016</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>915</th>\n",
       "    <th>0.122959</th>\n",
       "    <th>0.727480</th>\n",
       "    <th>66.006737</th>\n",
       "    <th>0.852831</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>916</th>\n",
       "    <th>0.121519</th>\n",
       "    <th>0.816069</th>\n",
       "    <th>79.551155</th>\n",
       "    <th>0.903176</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>917</th>\n",
       "    <th>0.120895</th>\n",
       "    <th>0.802148</th>\n",
       "    <th>79.725113</th>\n",
       "    <th>0.895292</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>918</th>\n",
       "    <th>0.122331</th>\n",
       "    <th>0.731320</th>\n",
       "    <th>71.896248</th>\n",
       "    <th>0.854683</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>919</th>\n",
       "    <th>0.124202</th>\n",
       "    <th>0.765659</th>\n",
       "    <th>71.717850</th>\n",
       "    <th>0.874894</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>920</th>\n",
       "    <th>0.117634</th>\n",
       "    <th>0.789400</th>\n",
       "    <th>74.646309</th>\n",
       "    <th>0.888323</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>921</th>\n",
       "    <th>0.121598</th>\n",
       "    <th>0.823749</th>\n",
       "    <th>77.870102</th>\n",
       "    <th>0.907297</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>922</th>\n",
       "    <th>0.119266</th>\n",
       "    <th>0.689247</th>\n",
       "    <th>56.827957</th>\n",
       "    <th>0.829969</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>923</th>\n",
       "    <th>0.118664</th>\n",
       "    <th>0.691824</th>\n",
       "    <th>62.945202</th>\n",
       "    <th>0.831548</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>924</th>\n",
       "    <th>0.121882</th>\n",
       "    <th>0.861561</th>\n",
       "    <th>68.371605</th>\n",
       "    <th>0.928117</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>925</th>\n",
       "    <th>0.121415</th>\n",
       "    <th>0.776154</th>\n",
       "    <th>65.963669</th>\n",
       "    <th>0.880535</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>926</th>\n",
       "    <th>0.116793</th>\n",
       "    <th>0.672379</th>\n",
       "    <th>63.294083</th>\n",
       "    <th>0.819957</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>927</th>\n",
       "    <th>0.117897</th>\n",
       "    <th>0.714725</th>\n",
       "    <th>66.252090</th>\n",
       "    <th>0.845114</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>928</th>\n",
       "    <th>0.124026</th>\n",
       "    <th>0.665319</th>\n",
       "    <th>62.029869</th>\n",
       "    <th>0.815544</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>929</th>\n",
       "    <th>0.118336</th>\n",
       "    <th>0.771087</th>\n",
       "    <th>73.835213</th>\n",
       "    <th>0.877774</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>930</th>\n",
       "    <th>0.119793</th>\n",
       "    <th>0.622166</th>\n",
       "    <th>60.023930</th>\n",
       "    <th>0.788672</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>931</th>\n",
       "    <th>0.117317</th>\n",
       "    <th>0.773960</th>\n",
       "    <th>68.661530</th>\n",
       "    <th>0.879605</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>932</th>\n",
       "    <th>0.114125</th>\n",
       "    <th>0.695153</th>\n",
       "    <th>67.658638</th>\n",
       "    <th>0.833332</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>933</th>\n",
       "    <th>0.115104</th>\n",
       "    <th>0.664740</th>\n",
       "    <th>63.792461</th>\n",
       "    <th>0.814830</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>934</th>\n",
       "    <th>0.116095</th>\n",
       "    <th>0.651460</th>\n",
       "    <th>59.220108</th>\n",
       "    <th>0.806791</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>935</th>\n",
       "    <th>0.117770</th>\n",
       "    <th>0.726300</th>\n",
       "    <th>67.102173</th>\n",
       "    <th>0.852068</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>936</th>\n",
       "    <th>0.119095</th>\n",
       "    <th>0.751121</th>\n",
       "    <th>69.539070</th>\n",
       "    <th>0.866558</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>937</th>\n",
       "    <th>0.118085</th>\n",
       "    <th>0.841159</th>\n",
       "    <th>76.659012</th>\n",
       "    <th>0.916974</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>938</th>\n",
       "    <th>0.117928</th>\n",
       "    <th>0.780033</th>\n",
       "    <th>73.175438</th>\n",
       "    <th>0.882683</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>939</th>\n",
       "    <th>0.118104</th>\n",
       "    <th>0.685101</th>\n",
       "    <th>69.956955</th>\n",
       "    <th>0.827517</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>940</th>\n",
       "    <th>0.120478</th>\n",
       "    <th>0.771169</th>\n",
       "    <th>76.381409</th>\n",
       "    <th>0.877659</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>941</th>\n",
       "    <th>0.117634</th>\n",
       "    <th>0.717389</th>\n",
       "    <th>64.905418</th>\n",
       "    <th>0.846399</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>942</th>\n",
       "    <th>0.114946</th>\n",
       "    <th>0.748336</th>\n",
       "    <th>65.818687</th>\n",
       "    <th>0.864469</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>943</th>\n",
       "    <th>0.118481</th>\n",
       "    <th>0.811096</th>\n",
       "    <th>73.063972</th>\n",
       "    <th>0.900270</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>944</th>\n",
       "    <th>0.119836</th>\n",
       "    <th>0.693087</th>\n",
       "    <th>67.192596</th>\n",
       "    <th>0.832341</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>945</th>\n",
       "    <th>0.117801</th>\n",
       "    <th>0.709374</th>\n",
       "    <th>69.899551</th>\n",
       "    <th>0.841849</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>946</th>\n",
       "    <th>0.118832</th>\n",
       "    <th>0.657450</th>\n",
       "    <th>56.023426</th>\n",
       "    <th>0.810399</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>947</th>\n",
       "    <th>0.113406</th>\n",
       "    <th>0.735711</th>\n",
       "    <th>60.315777</th>\n",
       "    <th>0.857450</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>948</th>\n",
       "    <th>0.115026</th>\n",
       "    <th>0.741877</th>\n",
       "    <th>63.647175</th>\n",
       "    <th>0.860206</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>949</th>\n",
       "    <th>0.114263</th>\n",
       "    <th>0.667443</th>\n",
       "    <th>56.309727</th>\n",
       "    <th>0.816779</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>950</th>\n",
       "    <th>0.118397</th>\n",
       "    <th>0.632594</th>\n",
       "    <th>57.012901</th>\n",
       "    <th>0.795348</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>951</th>\n",
       "    <th>0.111560</th>\n",
       "    <th>0.719670</th>\n",
       "    <th>66.968857</th>\n",
       "    <th>0.847906</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>952</th>\n",
       "    <th>0.114859</th>\n",
       "    <th>0.669670</th>\n",
       "    <th>63.329250</th>\n",
       "    <th>0.818244</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>953</th>\n",
       "    <th>0.119110</th>\n",
       "    <th>0.671627</th>\n",
       "    <th>61.439075</th>\n",
       "    <th>0.819363</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>954</th>\n",
       "    <th>0.116624</th>\n",
       "    <th>0.675068</th>\n",
       "    <th>64.733635</th>\n",
       "    <th>0.821046</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>955</th>\n",
       "    <th>0.115915</th>\n",
       "    <th>0.764605</th>\n",
       "    <th>76.854065</th>\n",
       "    <th>0.874040</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>956</th>\n",
       "    <th>0.121648</th>\n",
       "    <th>0.687593</th>\n",
       "    <th>65.689209</th>\n",
       "    <th>0.829128</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>957</th>\n",
       "    <th>0.119421</th>\n",
       "    <th>0.697026</th>\n",
       "    <th>59.113720</th>\n",
       "    <th>0.834478</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>958</th>\n",
       "    <th>0.115595</th>\n",
       "    <th>0.706061</th>\n",
       "    <th>72.140297</th>\n",
       "    <th>0.840119</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>959</th>\n",
       "    <th>0.116709</th>\n",
       "    <th>0.685859</th>\n",
       "    <th>55.077888</th>\n",
       "    <th>0.827981</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>960</th>\n",
       "    <th>0.116936</th>\n",
       "    <th>0.703268</th>\n",
       "    <th>57.799576</th>\n",
       "    <th>0.838525</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>961</th>\n",
       "    <th>0.116535</th>\n",
       "    <th>0.711215</th>\n",
       "    <th>60.594662</th>\n",
       "    <th>0.842464</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>962</th>\n",
       "    <th>0.115108</th>\n",
       "    <th>0.724686</th>\n",
       "    <th>68.987823</th>\n",
       "    <th>0.850802</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>963</th>\n",
       "    <th>0.116769</th>\n",
       "    <th>0.661107</th>\n",
       "    <th>64.263542</th>\n",
       "    <th>0.812487</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>964</th>\n",
       "    <th>0.113600</th>\n",
       "    <th>0.703931</th>\n",
       "    <th>67.577583</th>\n",
       "    <th>0.838645</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>965</th>\n",
       "    <th>0.113771</th>\n",
       "    <th>0.661129</th>\n",
       "    <th>64.234398</th>\n",
       "    <th>0.812776</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>966</th>\n",
       "    <th>0.115027</th>\n",
       "    <th>0.697155</th>\n",
       "    <th>69.484673</th>\n",
       "    <th>0.834809</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>967</th>\n",
       "    <th>0.114927</th>\n",
       "    <th>0.779625</th>\n",
       "    <th>69.883339</th>\n",
       "    <th>0.882858</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>968</th>\n",
       "    <th>0.114793</th>\n",
       "    <th>0.673794</th>\n",
       "    <th>59.328445</th>\n",
       "    <th>0.820541</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>969</th>\n",
       "    <th>0.113556</th>\n",
       "    <th>0.696457</th>\n",
       "    <th>59.943825</th>\n",
       "    <th>0.834325</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>970</th>\n",
       "    <th>0.115090</th>\n",
       "    <th>0.705856</th>\n",
       "    <th>67.017387</th>\n",
       "    <th>0.840031</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>971</th>\n",
       "    <th>0.115081</th>\n",
       "    <th>0.703994</th>\n",
       "    <th>63.391975</th>\n",
       "    <th>0.838366</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>972</th>\n",
       "    <th>0.113798</th>\n",
       "    <th>0.673404</th>\n",
       "    <th>61.300282</th>\n",
       "    <th>0.820445</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>973</th>\n",
       "    <th>0.115016</th>\n",
       "    <th>0.703637</th>\n",
       "    <th>66.659164</th>\n",
       "    <th>0.838355</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>974</th>\n",
       "    <th>0.114694</th>\n",
       "    <th>0.614552</th>\n",
       "    <th>55.502102</th>\n",
       "    <th>0.783201</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>975</th>\n",
       "    <th>0.115214</th>\n",
       "    <th>0.701869</th>\n",
       "    <th>62.894051</th>\n",
       "    <th>0.837469</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>976</th>\n",
       "    <th>0.112756</th>\n",
       "    <th>0.657801</th>\n",
       "    <th>61.919682</th>\n",
       "    <th>0.810937</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>977</th>\n",
       "    <th>0.113553</th>\n",
       "    <th>0.689850</th>\n",
       "    <th>64.984772</th>\n",
       "    <th>0.830112</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>978</th>\n",
       "    <th>0.114204</th>\n",
       "    <th>0.715862</th>\n",
       "    <th>60.413063</th>\n",
       "    <th>0.845453</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>979</th>\n",
       "    <th>0.116184</th>\n",
       "    <th>0.730430</th>\n",
       "    <th>62.707680</th>\n",
       "    <th>0.853831</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>980</th>\n",
       "    <th>0.114757</th>\n",
       "    <th>0.703285</th>\n",
       "    <th>62.496994</th>\n",
       "    <th>0.838057</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>981</th>\n",
       "    <th>0.113578</th>\n",
       "    <th>0.663716</th>\n",
       "    <th>60.079155</th>\n",
       "    <th>0.814641</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>982</th>\n",
       "    <th>0.116264</th>\n",
       "    <th>0.668989</th>\n",
       "    <th>62.524399</th>\n",
       "    <th>0.817625</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>983</th>\n",
       "    <th>0.114423</th>\n",
       "    <th>0.663821</th>\n",
       "    <th>60.894039</th>\n",
       "    <th>0.814687</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>984</th>\n",
       "    <th>0.110721</th>\n",
       "    <th>0.747818</th>\n",
       "    <th>71.188477</th>\n",
       "    <th>0.864558</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>985</th>\n",
       "    <th>0.111381</th>\n",
       "    <th>0.807214</th>\n",
       "    <th>66.767303</th>\n",
       "    <th>0.898053</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>986</th>\n",
       "    <th>0.116541</th>\n",
       "    <th>0.664790</th>\n",
       "    <th>60.660988</th>\n",
       "    <th>0.815089</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>987</th>\n",
       "    <th>0.113688</th>\n",
       "    <th>0.686736</th>\n",
       "    <th>62.330536</th>\n",
       "    <th>0.828502</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>988</th>\n",
       "    <th>0.112749</th>\n",
       "    <th>0.665166</th>\n",
       "    <th>58.630375</th>\n",
       "    <th>0.815163</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>989</th>\n",
       "    <th>0.112212</th>\n",
       "    <th>0.689947</th>\n",
       "    <th>58.273819</th>\n",
       "    <th>0.830407</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>990</th>\n",
       "    <th>0.112025</th>\n",
       "    <th>0.719338</th>\n",
       "    <th>69.021492</th>\n",
       "    <th>0.847998</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>991</th>\n",
       "    <th>0.113327</th>\n",
       "    <th>0.651904</th>\n",
       "    <th>66.724060</th>\n",
       "    <th>0.807113</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>992</th>\n",
       "    <th>0.110513</th>\n",
       "    <th>0.674112</th>\n",
       "    <th>62.859386</th>\n",
       "    <th>0.820740</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>993</th>\n",
       "    <th>0.112400</th>\n",
       "    <th>0.727665</th>\n",
       "    <th>74.631126</th>\n",
       "    <th>0.852418</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>994</th>\n",
       "    <th>0.112986</th>\n",
       "    <th>0.664349</th>\n",
       "    <th>62.482075</th>\n",
       "    <th>0.814801</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>995</th>\n",
       "    <th>0.115632</th>\n",
       "    <th>0.718697</th>\n",
       "    <th>62.276108</th>\n",
       "    <th>0.847678</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>996</th>\n",
       "    <th>0.111128</th>\n",
       "    <th>0.719331</th>\n",
       "    <th>64.033096</th>\n",
       "    <th>0.847973</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>997</th>\n",
       "    <th>0.113917</th>\n",
       "    <th>0.721044</th>\n",
       "    <th>63.799362</th>\n",
       "    <th>0.848813</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>998</th>\n",
       "    <th>0.109593</th>\n",
       "    <th>0.696601</th>\n",
       "    <th>67.893372</th>\n",
       "    <th>0.834553</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>999</th>\n",
       "    <th>0.112950</th>\n",
       "    <th>0.680917</th>\n",
       "    <th>62.757217</th>\n",
       "    <th>0.824996</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1000</th>\n",
       "    <th>0.111084</th>\n",
       "    <th>0.692478</th>\n",
       "    <th>60.775936</th>\n",
       "    <th>0.832038</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1001</th>\n",
       "    <th>0.114513</th>\n",
       "    <th>0.695487</th>\n",
       "    <th>66.445297</th>\n",
       "    <th>0.833748</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1002</th>\n",
       "    <th>0.113680</th>\n",
       "    <th>0.647071</th>\n",
       "    <th>55.280186</th>\n",
       "    <th>0.803783</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1003</th>\n",
       "    <th>0.108269</th>\n",
       "    <th>0.716334</th>\n",
       "    <th>74.150452</th>\n",
       "    <th>0.846165</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1004</th>\n",
       "    <th>0.110694</th>\n",
       "    <th>0.724959</th>\n",
       "    <th>66.443893</th>\n",
       "    <th>0.851275</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1005</th>\n",
       "    <th>0.113870</th>\n",
       "    <th>0.728502</th>\n",
       "    <th>70.775101</th>\n",
       "    <th>0.853326</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1006</th>\n",
       "    <th>0.108967</th>\n",
       "    <th>0.716323</th>\n",
       "    <th>64.946159</th>\n",
       "    <th>0.845700</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1007</th>\n",
       "    <th>0.113673</th>\n",
       "    <th>0.661401</th>\n",
       "    <th>63.587795</th>\n",
       "    <th>0.813233</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1008</th>\n",
       "    <th>0.113013</th>\n",
       "    <th>0.638693</th>\n",
       "    <th>61.157848</th>\n",
       "    <th>0.798917</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1009</th>\n",
       "    <th>0.115533</th>\n",
       "    <th>0.626402</th>\n",
       "    <th>60.000439</th>\n",
       "    <th>0.790945</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1010</th>\n",
       "    <th>0.112777</th>\n",
       "    <th>0.639446</th>\n",
       "    <th>58.681961</th>\n",
       "    <th>0.799542</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1011</th>\n",
       "    <th>0.113420</th>\n",
       "    <th>0.745895</th>\n",
       "    <th>71.443947</th>\n",
       "    <th>0.863412</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1012</th>\n",
       "    <th>0.115846</th>\n",
       "    <th>0.697263</th>\n",
       "    <th>66.612900</th>\n",
       "    <th>0.834739</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1013</th>\n",
       "    <th>0.110823</th>\n",
       "    <th>0.670315</th>\n",
       "    <th>66.377922</th>\n",
       "    <th>0.818662</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1014</th>\n",
       "    <th>0.109037</th>\n",
       "    <th>0.732495</th>\n",
       "    <th>66.937401</th>\n",
       "    <th>0.855548</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1015</th>\n",
       "    <th>0.113066</th>\n",
       "    <th>0.669594</th>\n",
       "    <th>58.175838</th>\n",
       "    <th>0.818005</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1016</th>\n",
       "    <th>0.113751</th>\n",
       "    <th>0.712269</th>\n",
       "    <th>61.924576</th>\n",
       "    <th>0.843643</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1017</th>\n",
       "    <th>0.113364</th>\n",
       "    <th>0.716702</th>\n",
       "    <th>66.051178</th>\n",
       "    <th>0.846051</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1018</th>\n",
       "    <th>0.110154</th>\n",
       "    <th>0.629613</th>\n",
       "    <th>53.948437</th>\n",
       "    <th>0.793282</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1019</th>\n",
       "    <th>0.113974</th>\n",
       "    <th>0.621548</th>\n",
       "    <th>55.322048</th>\n",
       "    <th>0.788056</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1020</th>\n",
       "    <th>0.110751</th>\n",
       "    <th>0.601124</th>\n",
       "    <th>52.246807</th>\n",
       "    <th>0.775088</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1021</th>\n",
       "    <th>0.109951</th>\n",
       "    <th>0.688833</th>\n",
       "    <th>67.933411</th>\n",
       "    <th>0.829391</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1022</th>\n",
       "    <th>0.110413</th>\n",
       "    <th>0.662017</th>\n",
       "    <th>60.156349</th>\n",
       "    <th>0.813275</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1023</th>\n",
       "    <th>0.106534</th>\n",
       "    <th>0.595584</th>\n",
       "    <th>55.279549</th>\n",
       "    <th>0.771388</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1024</th>\n",
       "    <th>0.110037</th>\n",
       "    <th>0.651853</th>\n",
       "    <th>54.517361</th>\n",
       "    <th>0.807061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1025</th>\n",
       "    <th>0.108328</th>\n",
       "    <th>0.648169</th>\n",
       "    <th>61.931644</th>\n",
       "    <th>0.804919</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1026</th>\n",
       "    <th>0.109239</th>\n",
       "    <th>0.707837</th>\n",
       "    <th>67.050537</th>\n",
       "    <th>0.840987</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1027</th>\n",
       "    <th>0.110592</th>\n",
       "    <th>0.696830</th>\n",
       "    <th>65.411705</th>\n",
       "    <th>0.834121</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1028</th>\n",
       "    <th>0.109364</th>\n",
       "    <th>0.616449</th>\n",
       "    <th>57.367199</th>\n",
       "    <th>0.784916</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1029</th>\n",
       "    <th>0.108515</th>\n",
       "    <th>0.650146</th>\n",
       "    <th>60.075970</th>\n",
       "    <th>0.806090</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1030</th>\n",
       "    <th>0.108698</th>\n",
       "    <th>0.626738</th>\n",
       "    <th>56.584175</th>\n",
       "    <th>0.791389</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1031</th>\n",
       "    <th>0.110545</th>\n",
       "    <th>0.661287</th>\n",
       "    <th>57.550724</th>\n",
       "    <th>0.812886</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1032</th>\n",
       "    <th>0.109648</th>\n",
       "    <th>0.716472</th>\n",
       "    <th>67.748405</th>\n",
       "    <th>0.846017</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1033</th>\n",
       "    <th>0.110666</th>\n",
       "    <th>0.721703</th>\n",
       "    <th>61.370411</th>\n",
       "    <th>0.849336</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1034</th>\n",
       "    <th>0.108052</th>\n",
       "    <th>0.702248</th>\n",
       "    <th>66.118401</th>\n",
       "    <th>0.837387</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1035</th>\n",
       "    <th>0.110191</th>\n",
       "    <th>0.675877</th>\n",
       "    <th>60.395775</th>\n",
       "    <th>0.822072</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1036</th>\n",
       "    <th>0.110227</th>\n",
       "    <th>0.748638</th>\n",
       "    <th>70.651085</th>\n",
       "    <th>0.864934</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1037</th>\n",
       "    <th>0.107478</th>\n",
       "    <th>0.702911</th>\n",
       "    <th>60.390820</th>\n",
       "    <th>0.838160</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1038</th>\n",
       "    <th>0.107014</th>\n",
       "    <th>0.682117</th>\n",
       "    <th>64.727257</th>\n",
       "    <th>0.825628</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1039</th>\n",
       "    <th>0.107988</th>\n",
       "    <th>0.690681</th>\n",
       "    <th>59.398838</th>\n",
       "    <th>0.830663</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1040</th>\n",
       "    <th>0.109406</th>\n",
       "    <th>0.726103</th>\n",
       "    <th>68.107109</th>\n",
       "    <th>0.851721</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1041</th>\n",
       "    <th>0.111461</th>\n",
       "    <th>0.713802</th>\n",
       "    <th>67.855278</th>\n",
       "    <th>0.843992</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1042</th>\n",
       "    <th>0.110008</th>\n",
       "    <th>0.738403</th>\n",
       "    <th>68.018517</th>\n",
       "    <th>0.859139</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1043</th>\n",
       "    <th>0.111682</th>\n",
       "    <th>0.695826</th>\n",
       "    <th>61.646255</th>\n",
       "    <th>0.833998</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1044</th>\n",
       "    <th>0.110149</th>\n",
       "    <th>0.707236</th>\n",
       "    <th>63.210236</th>\n",
       "    <th>0.840090</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1045</th>\n",
       "    <th>0.110188</th>\n",
       "    <th>0.673537</th>\n",
       "    <th>61.170101</th>\n",
       "    <th>0.820452</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1046</th>\n",
       "    <th>0.106136</th>\n",
       "    <th>0.675906</th>\n",
       "    <th>60.163101</th>\n",
       "    <th>0.821846</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1047</th>\n",
       "    <th>0.110024</th>\n",
       "    <th>0.675090</th>\n",
       "    <th>63.409237</th>\n",
       "    <th>0.821519</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1048</th>\n",
       "    <th>0.111450</th>\n",
       "    <th>0.694755</th>\n",
       "    <th>69.518837</th>\n",
       "    <th>0.833205</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1049</th>\n",
       "    <th>0.109484</th>\n",
       "    <th>0.693412</th>\n",
       "    <th>68.181328</th>\n",
       "    <th>0.832619</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1050</th>\n",
       "    <th>0.105962</th>\n",
       "    <th>0.723172</th>\n",
       "    <th>71.092850</th>\n",
       "    <th>0.850108</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1051</th>\n",
       "    <th>0.107035</th>\n",
       "    <th>0.717332</th>\n",
       "    <th>68.434723</th>\n",
       "    <th>0.846860</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1052</th>\n",
       "    <th>0.106150</th>\n",
       "    <th>0.690129</th>\n",
       "    <th>62.086918</th>\n",
       "    <th>0.830390</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1053</th>\n",
       "    <th>0.107229</th>\n",
       "    <th>0.700486</th>\n",
       "    <th>67.600021</th>\n",
       "    <th>0.836539</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1054</th>\n",
       "    <th>0.106542</th>\n",
       "    <th>0.700408</th>\n",
       "    <th>66.504951</th>\n",
       "    <th>0.836715</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1055</th>\n",
       "    <th>0.105653</th>\n",
       "    <th>0.699423</th>\n",
       "    <th>63.906570</th>\n",
       "    <th>0.836009</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1056</th>\n",
       "    <th>0.109556</th>\n",
       "    <th>0.693279</th>\n",
       "    <th>65.200073</th>\n",
       "    <th>0.832308</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1057</th>\n",
       "    <th>0.110989</th>\n",
       "    <th>0.643094</th>\n",
       "    <th>60.262108</th>\n",
       "    <th>0.801174</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1058</th>\n",
       "    <th>0.107899</th>\n",
       "    <th>0.678355</th>\n",
       "    <th>61.911251</th>\n",
       "    <th>0.823404</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1059</th>\n",
       "    <th>0.108437</th>\n",
       "    <th>0.657593</th>\n",
       "    <th>57.979900</th>\n",
       "    <th>0.810708</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1060</th>\n",
       "    <th>0.111720</th>\n",
       "    <th>0.623135</th>\n",
       "    <th>57.255482</th>\n",
       "    <th>0.788568</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1061</th>\n",
       "    <th>0.105845</th>\n",
       "    <th>0.673802</th>\n",
       "    <th>61.635654</th>\n",
       "    <th>0.820593</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1062</th>\n",
       "    <th>0.105884</th>\n",
       "    <th>0.663994</th>\n",
       "    <th>59.094601</th>\n",
       "    <th>0.814784</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1063</th>\n",
       "    <th>0.106755</th>\n",
       "    <th>0.631936</th>\n",
       "    <th>55.873631</th>\n",
       "    <th>0.794906</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1064</th>\n",
       "    <th>0.109200</th>\n",
       "    <th>0.683541</th>\n",
       "    <th>59.107258</th>\n",
       "    <th>0.826329</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1065</th>\n",
       "    <th>0.108799</th>\n",
       "    <th>0.697166</th>\n",
       "    <th>60.864975</th>\n",
       "    <th>0.834405</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1066</th>\n",
       "    <th>0.108162</th>\n",
       "    <th>0.679611</th>\n",
       "    <th>58.756157</th>\n",
       "    <th>0.824043</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1067</th>\n",
       "    <th>0.106922</th>\n",
       "    <th>0.693146</th>\n",
       "    <th>58.279892</th>\n",
       "    <th>0.831820</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1068</th>\n",
       "    <th>0.108234</th>\n",
       "    <th>0.661890</th>\n",
       "    <th>58.878780</th>\n",
       "    <th>0.813535</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1069</th>\n",
       "    <th>0.108920</th>\n",
       "    <th>0.647930</th>\n",
       "    <th>56.688477</th>\n",
       "    <th>0.804521</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1070</th>\n",
       "    <th>0.109097</th>\n",
       "    <th>0.673447</th>\n",
       "    <th>62.437794</th>\n",
       "    <th>0.820384</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1071</th>\n",
       "    <th>0.109302</th>\n",
       "    <th>0.659194</th>\n",
       "    <th>58.287655</th>\n",
       "    <th>0.811860</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1072</th>\n",
       "    <th>0.109136</th>\n",
       "    <th>0.677154</th>\n",
       "    <th>62.214500</th>\n",
       "    <th>0.822631</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1073</th>\n",
       "    <th>0.106175</th>\n",
       "    <th>0.687666</th>\n",
       "    <th>62.000080</th>\n",
       "    <th>0.828926</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1074</th>\n",
       "    <th>0.108152</th>\n",
       "    <th>0.676504</th>\n",
       "    <th>61.541306</th>\n",
       "    <th>0.822206</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1075</th>\n",
       "    <th>0.109337</th>\n",
       "    <th>0.693769</th>\n",
       "    <th>62.072330</th>\n",
       "    <th>0.832747</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1076</th>\n",
       "    <th>0.104329</th>\n",
       "    <th>0.660901</th>\n",
       "    <th>59.947762</th>\n",
       "    <th>0.812639</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1077</th>\n",
       "    <th>0.105230</th>\n",
       "    <th>0.732236</th>\n",
       "    <th>67.541862</th>\n",
       "    <th>0.855634</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1078</th>\n",
       "    <th>0.105147</th>\n",
       "    <th>0.677211</th>\n",
       "    <th>61.423275</th>\n",
       "    <th>0.822420</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1079</th>\n",
       "    <th>0.106023</th>\n",
       "    <th>0.673557</th>\n",
       "    <th>59.437977</th>\n",
       "    <th>0.820588</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1080</th>\n",
       "    <th>0.104371</th>\n",
       "    <th>0.679835</th>\n",
       "    <th>60.936131</th>\n",
       "    <th>0.824433</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1081</th>\n",
       "    <th>0.106817</th>\n",
       "    <th>0.643510</th>\n",
       "    <th>56.378914</th>\n",
       "    <th>0.801805</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1082</th>\n",
       "    <th>0.105415</th>\n",
       "    <th>0.629687</th>\n",
       "    <th>57.273369</th>\n",
       "    <th>0.793436</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1083</th>\n",
       "    <th>0.107084</th>\n",
       "    <th>0.687497</th>\n",
       "    <th>63.435608</th>\n",
       "    <th>0.828780</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1084</th>\n",
       "    <th>0.107208</th>\n",
       "    <th>0.675498</th>\n",
       "    <th>62.246037</th>\n",
       "    <th>0.821577</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1085</th>\n",
       "    <th>0.106632</th>\n",
       "    <th>0.704333</th>\n",
       "    <th>62.489525</th>\n",
       "    <th>0.838713</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1086</th>\n",
       "    <th>0.103562</th>\n",
       "    <th>0.712755</th>\n",
       "    <th>63.820599</th>\n",
       "    <th>0.843980</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1087</th>\n",
       "    <th>0.106870</th>\n",
       "    <th>0.718676</th>\n",
       "    <th>64.974655</th>\n",
       "    <th>0.847621</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1088</th>\n",
       "    <th>0.105173</th>\n",
       "    <th>0.637784</th>\n",
       "    <th>56.179050</th>\n",
       "    <th>0.798471</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1089</th>\n",
       "    <th>0.104018</th>\n",
       "    <th>0.689378</th>\n",
       "    <th>60.056961</th>\n",
       "    <th>0.829875</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1090</th>\n",
       "    <th>0.107031</th>\n",
       "    <th>0.710522</th>\n",
       "    <th>62.294357</th>\n",
       "    <th>0.842544</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1091</th>\n",
       "    <th>0.105412</th>\n",
       "    <th>0.687441</th>\n",
       "    <th>63.063351</th>\n",
       "    <th>0.828904</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1092</th>\n",
       "    <th>0.105692</th>\n",
       "    <th>0.670642</th>\n",
       "    <th>61.048313</th>\n",
       "    <th>0.818848</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1093</th>\n",
       "    <th>0.102850</th>\n",
       "    <th>0.640987</th>\n",
       "    <th>60.003826</th>\n",
       "    <th>0.800379</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1094</th>\n",
       "    <th>0.107203</th>\n",
       "    <th>0.683166</th>\n",
       "    <th>63.222614</th>\n",
       "    <th>0.825690</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1095</th>\n",
       "    <th>0.104189</th>\n",
       "    <th>0.695009</th>\n",
       "    <th>67.003403</th>\n",
       "    <th>0.833569</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1096</th>\n",
       "    <th>0.103466</th>\n",
       "    <th>0.675452</th>\n",
       "    <th>65.622917</th>\n",
       "    <th>0.821270</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1097</th>\n",
       "    <th>0.105225</th>\n",
       "    <th>0.724957</th>\n",
       "    <th>71.218498</th>\n",
       "    <th>0.851078</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1098</th>\n",
       "    <th>0.105729</th>\n",
       "    <th>0.699567</th>\n",
       "    <th>65.197617</th>\n",
       "    <th>0.835857</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1099</th>\n",
       "    <th>0.103115</th>\n",
       "    <th>0.663635</th>\n",
       "    <th>61.898689</th>\n",
       "    <th>0.814576</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1100</th>\n",
       "    <th>0.102816</th>\n",
       "    <th>0.685389</th>\n",
       "    <th>62.144707</th>\n",
       "    <th>0.827651</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1101</th>\n",
       "    <th>0.105137</th>\n",
       "    <th>0.669069</th>\n",
       "    <th>62.819530</th>\n",
       "    <th>0.817756</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1102</th>\n",
       "    <th>0.105772</th>\n",
       "    <th>0.674110</th>\n",
       "    <th>63.492130</th>\n",
       "    <th>0.820673</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1103</th>\n",
       "    <th>0.107906</th>\n",
       "    <th>0.658794</th>\n",
       "    <th>57.200062</th>\n",
       "    <th>0.810734</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1104</th>\n",
       "    <th>0.105887</th>\n",
       "    <th>0.674157</th>\n",
       "    <th>60.639843</th>\n",
       "    <th>0.820823</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1105</th>\n",
       "    <th>0.105918</th>\n",
       "    <th>0.658582</th>\n",
       "    <th>58.905724</th>\n",
       "    <th>0.811026</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1106</th>\n",
       "    <th>0.105836</th>\n",
       "    <th>0.649627</th>\n",
       "    <th>58.043644</th>\n",
       "    <th>0.805798</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1107</th>\n",
       "    <th>0.105568</th>\n",
       "    <th>0.714683</th>\n",
       "    <th>70.111183</th>\n",
       "    <th>0.845331</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1108</th>\n",
       "    <th>0.105678</th>\n",
       "    <th>0.720048</th>\n",
       "    <th>68.355591</th>\n",
       "    <th>0.848493</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1109</th>\n",
       "    <th>0.104799</th>\n",
       "    <th>0.673262</th>\n",
       "    <th>61.219707</th>\n",
       "    <th>0.820322</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1110</th>\n",
       "    <th>0.104972</th>\n",
       "    <th>0.662744</th>\n",
       "    <th>62.762913</th>\n",
       "    <th>0.813806</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1111</th>\n",
       "    <th>0.105133</th>\n",
       "    <th>0.690532</th>\n",
       "    <th>61.640652</th>\n",
       "    <th>0.830503</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1112</th>\n",
       "    <th>0.103486</th>\n",
       "    <th>0.664740</th>\n",
       "    <th>63.140549</th>\n",
       "    <th>0.814835</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1113</th>\n",
       "    <th>0.101623</th>\n",
       "    <th>0.684902</th>\n",
       "    <th>64.553047</th>\n",
       "    <th>0.827519</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1114</th>\n",
       "    <th>0.103549</th>\n",
       "    <th>0.663503</th>\n",
       "    <th>59.322552</th>\n",
       "    <th>0.814103</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1115</th>\n",
       "    <th>0.102538</th>\n",
       "    <th>0.682376</th>\n",
       "    <th>60.343124</th>\n",
       "    <th>0.825887</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1116</th>\n",
       "    <th>0.104272</th>\n",
       "    <th>0.681480</th>\n",
       "    <th>60.307819</th>\n",
       "    <th>0.825448</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1117</th>\n",
       "    <th>0.103168</th>\n",
       "    <th>0.644983</th>\n",
       "    <th>52.785774</th>\n",
       "    <th>0.802900</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1118</th>\n",
       "    <th>0.104473</th>\n",
       "    <th>0.680538</th>\n",
       "    <th>61.959587</th>\n",
       "    <th>0.824433</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1119</th>\n",
       "    <th>0.103827</th>\n",
       "    <th>0.683811</th>\n",
       "    <th>66.384262</th>\n",
       "    <th>0.826732</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1120</th>\n",
       "    <th>0.105846</th>\n",
       "    <th>0.646476</th>\n",
       "    <th>58.515076</th>\n",
       "    <th>0.803625</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1121</th>\n",
       "    <th>0.105947</th>\n",
       "    <th>0.659887</th>\n",
       "    <th>59.862263</th>\n",
       "    <th>0.812283</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1122</th>\n",
       "    <th>0.104574</th>\n",
       "    <th>0.672116</th>\n",
       "    <th>60.500851</th>\n",
       "    <th>0.819222</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1123</th>\n",
       "    <th>0.103727</th>\n",
       "    <th>0.690917</th>\n",
       "    <th>62.123569</th>\n",
       "    <th>0.830868</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1124</th>\n",
       "    <th>0.105882</th>\n",
       "    <th>0.695593</th>\n",
       "    <th>60.763470</th>\n",
       "    <th>0.833798</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1125</th>\n",
       "    <th>0.100477</th>\n",
       "    <th>0.685381</th>\n",
       "    <th>58.069469</th>\n",
       "    <th>0.827735</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1126</th>\n",
       "    <th>0.100181</th>\n",
       "    <th>0.658140</th>\n",
       "    <th>57.167770</th>\n",
       "    <th>0.810719</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1127</th>\n",
       "    <th>0.104611</th>\n",
       "    <th>0.674485</th>\n",
       "    <th>57.494442</th>\n",
       "    <th>0.821196</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1128</th>\n",
       "    <th>0.103275</th>\n",
       "    <th>0.690486</th>\n",
       "    <th>61.308189</th>\n",
       "    <th>0.830830</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1129</th>\n",
       "    <th>0.104447</th>\n",
       "    <th>0.679717</th>\n",
       "    <th>60.872749</th>\n",
       "    <th>0.824328</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1130</th>\n",
       "    <th>0.105742</th>\n",
       "    <th>0.677272</th>\n",
       "    <th>58.419724</th>\n",
       "    <th>0.822878</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1131</th>\n",
       "    <th>0.105764</th>\n",
       "    <th>0.672718</th>\n",
       "    <th>61.970825</th>\n",
       "    <th>0.820070</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1132</th>\n",
       "    <th>0.106349</th>\n",
       "    <th>0.680749</th>\n",
       "    <th>60.648876</th>\n",
       "    <th>0.824813</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1133</th>\n",
       "    <th>0.104641</th>\n",
       "    <th>0.658563</th>\n",
       "    <th>56.587170</th>\n",
       "    <th>0.811158</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1134</th>\n",
       "    <th>0.103383</th>\n",
       "    <th>0.663976</th>\n",
       "    <th>54.905663</th>\n",
       "    <th>0.814585</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1135</th>\n",
       "    <th>0.101461</th>\n",
       "    <th>0.670168</th>\n",
       "    <th>55.388039</th>\n",
       "    <th>0.818291</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1136</th>\n",
       "    <th>0.102809</th>\n",
       "    <th>0.663206</th>\n",
       "    <th>60.060989</th>\n",
       "    <th>0.813903</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1137</th>\n",
       "    <th>0.103380</th>\n",
       "    <th>0.663598</th>\n",
       "    <th>56.374207</th>\n",
       "    <th>0.814369</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1138</th>\n",
       "    <th>0.101517</th>\n",
       "    <th>0.656552</th>\n",
       "    <th>57.405998</th>\n",
       "    <th>0.810118</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1139</th>\n",
       "    <th>0.103785</th>\n",
       "    <th>0.683580</th>\n",
       "    <th>61.629467</th>\n",
       "    <th>0.826476</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1140</th>\n",
       "    <th>0.104309</th>\n",
       "    <th>0.654732</th>\n",
       "    <th>56.688705</th>\n",
       "    <th>0.808792</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1141</th>\n",
       "    <th>0.104922</th>\n",
       "    <th>0.667449</th>\n",
       "    <th>59.425304</th>\n",
       "    <th>0.816854</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1142</th>\n",
       "    <th>0.102536</th>\n",
       "    <th>0.693071</th>\n",
       "    <th>67.670326</th>\n",
       "    <th>0.832449</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1143</th>\n",
       "    <th>0.100758</th>\n",
       "    <th>0.646690</th>\n",
       "    <th>54.260036</th>\n",
       "    <th>0.803960</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1144</th>\n",
       "    <th>0.105538</th>\n",
       "    <th>0.657214</th>\n",
       "    <th>57.449348</th>\n",
       "    <th>0.810258</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1145</th>\n",
       "    <th>0.101753</th>\n",
       "    <th>0.665163</th>\n",
       "    <th>56.339130</th>\n",
       "    <th>0.815167</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1146</th>\n",
       "    <th>0.103004</th>\n",
       "    <th>0.645178</th>\n",
       "    <th>56.246162</th>\n",
       "    <th>0.803142</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1147</th>\n",
       "    <th>0.102237</th>\n",
       "    <th>0.668446</th>\n",
       "    <th>57.747051</th>\n",
       "    <th>0.817382</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1148</th>\n",
       "    <th>0.102180</th>\n",
       "    <th>0.665866</th>\n",
       "    <th>59.391396</th>\n",
       "    <th>0.815635</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1149</th>\n",
       "    <th>0.105951</th>\n",
       "    <th>0.654319</th>\n",
       "    <th>54.368999</th>\n",
       "    <th>0.808872</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1150</th>\n",
       "    <th>0.103076</th>\n",
       "    <th>0.655487</th>\n",
       "    <th>57.211899</th>\n",
       "    <th>0.808585</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1151</th>\n",
       "    <th>0.099158</th>\n",
       "    <th>0.682879</th>\n",
       "    <th>62.356533</th>\n",
       "    <th>0.826283</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1152</th>\n",
       "    <th>0.101845</th>\n",
       "    <th>0.675864</th>\n",
       "    <th>59.867569</th>\n",
       "    <th>0.821959</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1153</th>\n",
       "    <th>0.101236</th>\n",
       "    <th>0.672338</th>\n",
       "    <th>59.207954</th>\n",
       "    <th>0.819734</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1154</th>\n",
       "    <th>0.104897</th>\n",
       "    <th>0.663111</th>\n",
       "    <th>54.334721</th>\n",
       "    <th>0.814174</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1155</th>\n",
       "    <th>0.103444</th>\n",
       "    <th>0.655755</th>\n",
       "    <th>56.016613</th>\n",
       "    <th>0.809094</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1156</th>\n",
       "    <th>0.102822</th>\n",
       "    <th>0.662476</th>\n",
       "    <th>56.886436</th>\n",
       "    <th>0.813638</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1157</th>\n",
       "    <th>0.103356</th>\n",
       "    <th>0.663183</th>\n",
       "    <th>58.463024</th>\n",
       "    <th>0.814197</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1158</th>\n",
       "    <th>0.102910</th>\n",
       "    <th>0.672716</th>\n",
       "    <th>58.736458</th>\n",
       "    <th>0.820122</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1159</th>\n",
       "    <th>0.103234</th>\n",
       "    <th>0.679339</th>\n",
       "    <th>58.408337</th>\n",
       "    <th>0.823705</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1160</th>\n",
       "    <th>0.103107</th>\n",
       "    <th>0.678410</th>\n",
       "    <th>60.128101</th>\n",
       "    <th>0.823387</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1161</th>\n",
       "    <th>0.101940</th>\n",
       "    <th>0.668796</th>\n",
       "    <th>60.847252</th>\n",
       "    <th>0.817480</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1162</th>\n",
       "    <th>0.103096</th>\n",
       "    <th>0.650176</th>\n",
       "    <th>55.445995</th>\n",
       "    <th>0.806185</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1163</th>\n",
       "    <th>0.103919</th>\n",
       "    <th>0.639399</th>\n",
       "    <th>54.183998</th>\n",
       "    <th>0.799372</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1164</th>\n",
       "    <th>0.104967</th>\n",
       "    <th>0.668196</th>\n",
       "    <th>57.115814</th>\n",
       "    <th>0.817269</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1165</th>\n",
       "    <th>0.100913</th>\n",
       "    <th>0.661402</th>\n",
       "    <th>57.875488</th>\n",
       "    <th>0.812575</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1166</th>\n",
       "    <th>0.100585</th>\n",
       "    <th>0.666914</th>\n",
       "    <th>56.899414</th>\n",
       "    <th>0.815597</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1167</th>\n",
       "    <th>0.103432</th>\n",
       "    <th>0.642739</th>\n",
       "    <th>54.003281</th>\n",
       "    <th>0.801539</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1168</th>\n",
       "    <th>0.100148</th>\n",
       "    <th>0.645571</th>\n",
       "    <th>53.682407</th>\n",
       "    <th>0.803263</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1169</th>\n",
       "    <th>0.103059</th>\n",
       "    <th>0.655833</th>\n",
       "    <th>58.303867</th>\n",
       "    <th>0.809653</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1170</th>\n",
       "    <th>0.105150</th>\n",
       "    <th>0.649880</th>\n",
       "    <th>55.440018</th>\n",
       "    <th>0.805197</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1171</th>\n",
       "    <th>0.103878</th>\n",
       "    <th>0.680289</th>\n",
       "    <th>60.441952</th>\n",
       "    <th>0.824607</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1172</th>\n",
       "    <th>0.100066</th>\n",
       "    <th>0.688987</th>\n",
       "    <th>62.761875</th>\n",
       "    <th>0.830011</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1173</th>\n",
       "    <th>0.100683</th>\n",
       "    <th>0.643519</th>\n",
       "    <th>54.636375</th>\n",
       "    <th>0.801750</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1174</th>\n",
       "    <th>0.103695</th>\n",
       "    <th>0.662101</th>\n",
       "    <th>56.798450</th>\n",
       "    <th>0.813337</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1175</th>\n",
       "    <th>0.101431</th>\n",
       "    <th>0.654249</th>\n",
       "    <th>56.248837</th>\n",
       "    <th>0.808412</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1176</th>\n",
       "    <th>0.100923</th>\n",
       "    <th>0.659488</th>\n",
       "    <th>56.069420</th>\n",
       "    <th>0.811805</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1177</th>\n",
       "    <th>0.100472</th>\n",
       "    <th>0.668836</th>\n",
       "    <th>58.301693</th>\n",
       "    <th>0.817303</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1178</th>\n",
       "    <th>0.104858</th>\n",
       "    <th>0.706710</th>\n",
       "    <th>64.259979</th>\n",
       "    <th>0.840536</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1179</th>\n",
       "    <th>0.105970</th>\n",
       "    <th>0.665121</th>\n",
       "    <th>57.154869</th>\n",
       "    <th>0.815120</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1180</th>\n",
       "    <th>0.103628</th>\n",
       "    <th>0.660857</th>\n",
       "    <th>57.652969</th>\n",
       "    <th>0.812251</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1181</th>\n",
       "    <th>0.098558</th>\n",
       "    <th>0.648798</th>\n",
       "    <th>55.659714</th>\n",
       "    <th>0.805202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1182</th>\n",
       "    <th>0.100379</th>\n",
       "    <th>0.660021</th>\n",
       "    <th>56.983532</th>\n",
       "    <th>0.812224</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1183</th>\n",
       "    <th>0.102600</th>\n",
       "    <th>0.662823</th>\n",
       "    <th>57.869301</th>\n",
       "    <th>0.813742</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1184</th>\n",
       "    <th>0.100344</th>\n",
       "    <th>0.646766</th>\n",
       "    <th>54.533501</th>\n",
       "    <th>0.803965</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1185</th>\n",
       "    <th>0.102849</th>\n",
       "    <th>0.646764</th>\n",
       "    <th>52.892136</th>\n",
       "    <th>0.803796</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1186</th>\n",
       "    <th>0.103438</th>\n",
       "    <th>0.668249</th>\n",
       "    <th>58.565964</th>\n",
       "    <th>0.817379</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1187</th>\n",
       "    <th>0.102145</th>\n",
       "    <th>0.668463</th>\n",
       "    <th>58.578476</th>\n",
       "    <th>0.817300</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1188</th>\n",
       "    <th>0.103654</th>\n",
       "    <th>0.672041</th>\n",
       "    <th>58.621674</th>\n",
       "    <th>0.819339</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1189</th>\n",
       "    <th>0.101864</th>\n",
       "    <th>0.643152</th>\n",
       "    <th>54.239857</th>\n",
       "    <th>0.801821</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1190</th>\n",
       "    <th>0.101938</th>\n",
       "    <th>0.636780</th>\n",
       "    <th>51.671230</th>\n",
       "    <th>0.797895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1191</th>\n",
       "    <th>0.102562</th>\n",
       "    <th>0.666655</th>\n",
       "    <th>58.351452</th>\n",
       "    <th>0.816230</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1192</th>\n",
       "    <th>0.103810</th>\n",
       "    <th>0.649241</th>\n",
       "    <th>55.872070</th>\n",
       "    <th>0.805692</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1193</th>\n",
       "    <th>0.101447</th>\n",
       "    <th>0.653841</th>\n",
       "    <th>53.782349</th>\n",
       "    <th>0.808504</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1194</th>\n",
       "    <th>0.101819</th>\n",
       "    <th>0.654426</th>\n",
       "    <th>55.459511</th>\n",
       "    <th>0.808151</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1195</th>\n",
       "    <th>0.101880</th>\n",
       "    <th>0.662153</th>\n",
       "    <th>56.282032</th>\n",
       "    <th>0.813545</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1196</th>\n",
       "    <th>0.099991</th>\n",
       "    <th>0.653965</th>\n",
       "    <th>55.536018</th>\n",
       "    <th>0.808547</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1197</th>\n",
       "    <th>0.102185</th>\n",
       "    <th>0.632493</th>\n",
       "    <th>51.019947</th>\n",
       "    <th>0.794836</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1198</th>\n",
       "    <th>0.103757</th>\n",
       "    <th>0.672557</th>\n",
       "    <th>58.831612</th>\n",
       "    <th>0.819726</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1199</th>\n",
       "    <th>0.103230</th>\n",
       "    <th>0.644949</th>\n",
       "    <th>54.025200</th>\n",
       "    <th>0.802998</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1200</th>\n",
       "    <th>0.101254</th>\n",
       "    <th>0.668392</th>\n",
       "    <th>58.039326</th>\n",
       "    <th>0.817496</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.fit_one_cycle(int(environ['n_epochs']), float(environ['lr']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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UQy9pumZ8VYRQzjay24jII+bXh14T7rNHL0c8fvnQawHDVM83QjkAAAAcgCAOAAAwEqEcekmTJOatoZzni0uhnP7yrLgUymFK+Xy5r8HJczAUAACAQ/GaFAAAGE4oh17SJIls7fiqN/a6PietDDcZX8WUsluhHE6YA54AAAAAAACcvl6hnCRJPpIkyYPFx/9SkiR/KEmSF6ddNY5JmkTkbU05DzXlbE1TDvuQacrhHkiMrwIAAOAA6sdBjbICAAB20Lcp51siYp4kyUcj4usj4isi4q9OtlYcnTRJYt5WlVM15Qjl9CaUwz7k8yKYAycvcQAUAACAPcs7PgYAANhO31BOluf5bUT8zoj4M3me/2cR8f7pVotjk6abxlcJ5fRWhXKMr2JCWSbIwOmq9l1NOQAAAAAAAJyuvqGcmyRJ/s2I+Hci4tsWn7ucZpU4RmkSkbWlcq6eKy6Fcraw2I6acphSbnwV94mAGQAAAHtkfBUAADCSvqGc3x8R/0xE/PE8z38+SZKviIi/Mt1qcWzSJIms7QVoOiuCOUI5/RlfxT5kt0I5nLDa802iLQcAAIB9M74KAAAYx0WfK+V5/pMR8YciIpIkeVdEvDPP86+dcsU4LsX4qo4XoA/eGfH0jf2u0Ckrt6PxVUwp05TDPSCQAwAAAAAAwAnr1ZSTJMn3JUnyfJIk746IH42Ib0iS5E9Pu2ock6Ipp+OLD96pKWcbmnLYh3xe/IP7QFU4AAAA+7QyvupwqwEAAJy+vuOrXsjz/I2I+F0R8Q15nv/6iPit060Wx2aWxIamHKGc3qqmHKEcJpRlmnI4XStPN9pyAAAAAAAAOE19QzkXSZK8PyK+JiK+bcL14UgVTTlCOaOomnKMr2JC2a1QDveAQA4AAACHkHd8DAAAsJ2+oZz/PiK+MyI+kef5P0qS5Csj4uPTrRbHJkmSyLrO7wvlbEcoh33I50I5nLDaAc8kCQdAAQAA2KuV8VVekwIAAMNd9LlSnud/PSL+eu3/PxcR/9pUK8XxmaXrxlc9L5SzjSqUY3wVE8rmxWWeL0INcILsuwAAAAAAAJywXk05SZJ8WZIkfyNJki8kSfL5JEm+JUmSL5t65Tgea8dXPfOuiEcveddIb4vtpCmHKeWLUE4ZzoFT5vkFAACAvTK+CgAAGEff8VXfEBF/MyI+EBEfjIi/tfgcZyJJkph3TcJ514cjbp9EvPm5fa7S6dKUwz6U8+aMsOIUrYRwtOUAAACwZ94cAgAAjKRvKOe9eZ5/Q57nt4t/fzEi3jvhenFkZmlE3vVi9D0fKS5f+cT+VuiUDQ3lfMcfi/jE946/PtxP2W1xKZTDSRPIAQAA4MAEdAAAgB30DeW8lCTJ702SZLb493sj4uUpV4zjsnZ81bu/srh8WSinlyqUs+X4qh/8+oif/a7x14f7qRxfJZTDSWo+3zgACgAAwD4ZXwUAAIyjbyjn34uIr4mIz0XEZyPid0fE759qpTg+aZLEPOt4AfrChyJmVxGv/Nx+V+pUleGmbZty8sw7c+gvK0M588OuB+wiiYhEWw4AAAB75hgcAAAwkl6hnDzPfzHP89+R5/l78zz/0jzP/9WI+F0TrxtHJE2S7tei6SziXR82vqqvKpSzZVNOnmk9oT9NOQAAAAC7E9ABAAB20Lcpp80fHW0tOHppEt3jqyKKEVYva8rppRpfNaQpR+sJPWWL/Uwoh1O08nyTOAAKAADAnhlfBQAAjGOXUI55EmckTZOYrw3lfKQYX+XE6WZDQjl5rimH7WS3xaXHJCfNnxoAAAAAAACcrl1COc70npE0SSJbd4+/5ysjbp9EvPnZva3TyapCOVuMryqDFUI59GV8FSet+YTjTw4AAAD2qP4mJ294AgAAdnCx7otJkrwZ7WfCkoh4ZpI14iilSUS2LpXz7q8sLl/+RMTzH9jPSp2sxXbcqilnEazIjK+ip3Jfsc9wypKk+AcAAAB7JYgDAACMY20oJ8/zd+5rRThuRVPOhvFVERGvfCLiK/6F/azUqRo0vipbvYRNNOUAAAAAjEBABwAAGG6X8VWckTTdML7qhS+LmF1FvPJze1unkzVofFUZynEQgJ4yoRxOWPN3nd99AAAA7JPxVQAAwEiEcuglXUwP6Rxhlc6KsVVv/NL+VupUDWrKEbBgS0I53AvJ4h8AAADskyAOAAAwDqEcepklxUnRtSOsHr4Y8fbr7V97+pZ3lZTK7ZANacqZj78+3E+CXJw0zxcAAAAcC69RAQCA4YRy6CVNy1DOmis9fKE9lPP4lYg/+dGIn/3uaVbu1JShnEHjqwQs6KlqyhHk4oQlSfHPAVAAAAD2qf4y1BsNAQCAHQjl0EtSjq9a9yL0mY6mnLe+EHH7JOKNT0+zcqdm0PgqoRy2VDXlOHAEAAAAsB3HUwAAgHEI5dBLv/FVL0Q8ee3u528eF5fZ7QRrdoIGhXLy1e+FdfJckIvT1nyuES4DAADgYLwmBQAAhhPKoZc02WF8VRnKmQvlFAaMrypHEWVGEdFDfT8RyuGkJYt/AAAAsEfeHAIAAIxEKIdeyvFV83WpnIcvFmOqbp+ufv5aU84K46uYWi6Uwwn53I9HvPrJxicd/AQAAOBICOgAAAA7EMqhl1lapHLyTeOrIu625VTjq7ZohrnPdgrlOAhAD/WmHO1KHLtv/U8ivud/bP9aoiUHAACAQ8g7PgYAANiOUA699Btf9WJx2RnK0ZQTEbVQzhYhpSqUI2BBD/XHmqYcjt3N2xE3T7q/LpgDAADAvnljHAAAMBKhHHpJ+4yvemYRynny2urnrx8tvlkoJyKWL+qNr2IqxldxSvLs7sFOBz8BAAA4Fl6jAgAAOxDKoZd0p/FViwYETTmFQU0589XvhXWy2n5in+Ho5Wv206T45wAoAAAAe+V1KAAAMA6hHHrZbnxVoymnGl+1RQjlPtOUw9RWmnIcROLI5etCOQAAAHAAjqcAAAAjEcqhl2p8Va+mnK5QzjyIWlPONqGcxXa3Demjvp/k9hmOXJ61hHIWv/MWgVDvUAQAAOBgBHQAAIAdCOXQS9WUs64qp2t81fUilLPNuKZ7rWzK2WZ8laYctlAfFWef4ehtaMqpgjkAAACwL4I4AADAOIRy6KUM5ax9Y8jlw4iLhxFPuppybu9+zzka1JRThnIcEKCHlfFVQjkcubamHL/rAAAAOKSV16VeowIAAMMJ5dBLuthT1o6viijacppNOVUoR1NORNRCOTf9TzyX44gELOgjE8rhhOSxZj9dtOQI6QAAAHAoXpMCAAA7EMqhl2p81cZQzovd46vqQYFzVp18zvtvk6opxzakh3rAQSiHo7dhfFUYXwUAAMC+CeIAAADjEMqhlyqUk/VpyjG+aq16sKnvCKsqlCNgQQ/1sJcwHMcuz1redejgJwAAAMfCa1QAAGA4oRx6maVlU86GK64bXzU3vioiVoM1QjlMoR6As89w7PI1TTlJsijKcQAUAACAPaq/ecT4KgAAYAdCOfSyyORsHl/1zIsRTxpNOdeaclashHJ6BpXK79F6Qh/1MWcOHHH0WkI59lsAAAAOyutSAABgHEI59JIsxlfNe42v6mjKEcopGF/F1OrhLfsMxy7P1uyni0SokA4AAAAH4zUpAAAwnFAOvcwWoZyN50UfvliEcupXFMpp2CWU4yAAPdQDDkI5HLt146siogrmAAAAwL44BgcAAIxEKIde0sWesnF81cMXitE5128tP1eOr+o7qum+22V8lYAFfdQDcLmRZxy7tlCOg58AAAAcUu11qYAOAACwA6EceqnGV/UJ5UQsR1jluaacpjyLqvlh66YcAQt6ML6KU7JufFVStuQ4AAoAAMCheE0KAAAMJ5RDL8vxVRtehD7zYnFZhnJu347qhatQTiHPIi4eFh9vHcoRsKCHXCiHE9I2vqr+XJMYXwUAAMCeaccBAABGIpRDL2nZlLPp/H7ZlPPkteKyHF0VIZRTyvOIi6viY+OrmIKmHE5KvuZgp0AOAAAAh2B8FQAAMA6hHHp5+dHTiIj4zGuP11/xmXcXl4++WFze1K7fN4By3w1pyilDFgIW9FFvysmMPOPIrRtfFRERiQOgAAAAHJDXpAAAwHBCOfTywRefiYiI9zz7YP0V3/PRiEgivvix4v/1UI5wQCHPIi4W23Hb8VW2IX2sNOU4cMSRy6MllGO/BQAA4IAcTwEAAEYilEMvs7QcX7XhBenVOyLe85GIz/948f8ylDO7isg05UREoymn7/iqfPUS1jG+ipOSd++nSbK8DgAAAOyN8VUAAMA4hHLo5SItdpWNoZyIiPf9yojP/0Tx8fUilPPg+YjsdqK1OzV5xGzRlNM3qFSesBawoI9cKIcT0ja+qv5UUwVzAAAAAAAA4LQI5dDLIpMTt71COb8q4pWfj3j61rIp5+Hz/Vth7rs8Hz6+SsCCPjTlcEryNU05IZADAADAAeSd/wEAANiKUA69bN2UE3nEF35qGcp58PwyKPD//NcR3/HHplnRUzBofNV89RLWqbdS2Wc4dm1NOXeu4wAoAAAAB+I1KQAAsAOhHHqZpUVbwbzPi9D3/cri8vM/vhxf9fCF5aimT/3DiE//ownW8kTkmaYcplXfT+wzjGl+G/Gtf7BoQxtNW1POyvyqEW8LAAAA+hDEAQAAxiGUQy8XZSgn63GC/4VfFnH1zojP/0TEzaPicw+fX7Z3zK+XAZ1zZHwVUzO+iqm88emIf/yXIz75A+MtM8+733WYCOQAAABwACuvUwV0AACA4YRy6KVsyrmd93gRmqYR7/uqRSjnSfG5By8UDQsRRRBlftv9/ffdSlNO3/FVi2BFZhQRPeRCOUykPCg55n7VNr6qfvAzScIBUAAAAA7G+CoAAGAHQjn0cjErm3J6vgh9368qQjnXLU05t+felJNFzIY25TgIQA8rTTn2GcY0QSindXxVSVMOAAAAh+B4CgAAMA6hHHqpmnL6hnI+8Gsinr4e8dkfKwIos6tlEGd+3b8h5j5aacrpGcrJjK9iC1mtiUq7EmOqmnJG3K/ydaGcxu0CAADAPhhfBQAAjEQoh14u0mJX6d2U86GvLi4/+XciLp+JmF0ugwLz69XQwNnJi5BSxPbjq4RyjtNLH4947VOHXoul+n5in2EKY4Zk2sZXrRzw1JYDAAAAAADAaRLKoZetm3Le89GIZ94V8fSNiKtnI9KL4qRrlgnl5FlEOotILweMr9J6cpT+xn8U8W1/5NBrsbQyvkoohxHlex5fJY8DAADAQdSOgWpvBQAAdiCUQy9lKGee9TwRm6YRH/rNxceX7yhCORFFGOfsx1flEUlatOVoyrkfbp5EfOaHj+cgTS6Uw1TWhHKyLOKLP7Pl4jqWd+exdCSPLQAAAM7DsRzjAQAATp5QDr1cbNuUExHxod9UXF4+sxrKub2OyM45lJMtQjlDmnIELI5SnkU8eSXijc8cek0KmnKYSnlQMmtp7fr4d0b8+d8c8cZnt19eZwtYEpGoywEAAAAAAOA0CeXQS9mUk20Vyvnq4vLq2SKAElGEcebX7Sd0z0UZkphdbRHKma9+L0dm8bj47I8ddjVK9fFwRp4xpnUBwSevFp9/+sY2C1xceAciAAAAx8T4KgAAYBxCOfQySwY05Xzg1xYNOfWmnNvrIiRgfNXw8VUOBByf8j753JGEcoyvYjLrxlctwmDbhC67xletjKtK/N4DAAAAAADgJF0cegU4DWmaRJpEzLcJ5Vy9I+KX/7aI93wkIp0Vn7t5VFwaXzVsfFXEItRjnMtRKe+fo2nKaewvMJbOEE0swzj1pqaNy9swms/vOgAAAA5h5XiKYysAAMBwQjls5eVHPUMkpd/zjcXlD/3vxeXNk+Iyuz3fcEkVytmmKademZuFkqtjU46v+tHDrkZJUw6TKUM5LW04VVPOFqGcruadO2EyB0ABAADYJ+OrAACAcTizT2/blOTcUY6vun5cW+AWI07ukzwrwkhDxldFtJ8M57DKgzNvfDri8SuHXZeIIhSRzKIY+yOUw4iqppyWJ4RNrTdrl9f1Pcl5hjcBAAAAAAC4F4Ry6O3Zq1lczQbuMrPL4rIcXxVxxiOs8qIp5+rZiLdf7/ctmeaTo5ZnEc++t/j4GNpysnkxMi5Jzzf8xkTWja8a0JQzJMgDAAAAUzO+CgAAGIlQDr1dXaQxH1qXk86Ky3pTTt+WmPumHF/1ri+PeO0Xu6/36R+K+J8/XDSvrDTlOHl9fPKId3+k+PD1Tx92VSKKNqVkEcqxvzCm8qBkW9ir/NwY46uaBzxVhQMAALBXxlcBAADjEMqht1maxHzoi9C0rSlnmxO390S5/ZI04sUvL8YddYWTXvm5iCevRrz1eaGcY5dnyxFtx/DuqSxbNuXYXxjTumabqilni3amTeOrkiQijK8CAAAAAADgNAnl0FuaJJENbspZBBbOvSmnOvGcFE05eRbx+qfWXze7XT1hPfY4op/6W+sbe9gsj4h08ev0GN49lS/GV6UzoRxGtiZEkw9oylkJHB7BYwcAAAAijK8CAABGI5RDb7M0GT6+alY25dRCOefelPOuDxcfv/rJjuuWJ7jn0zblfPMfiPjhvzTuMs9OXtyn5ceHlt0aX8U01jXbZGWQcJvgYL0OvC2gkyzaco7gcQUAAMAZ8ToUAAAYh1AOvaXJLuOrZsXldX181Rk35SRJMb4qIuLVX2i/bnliO58vAzoR47dJzJ+eZ2vRmPKsCMGUHx9aNje+iomUoZyW4E01vmpoU459FQAAgCOk2RUAANiBUA69Xcx2aMpJW5py5ufYlFOGctKI5z9QbJfXOkI51fiqbPXF/5gnrte1XtBfni+DZ8dwoCafL5pyEvct4yp377b9PJ+vXvZaXtfvtsbyj+BhBQAAwBkxvgoAABiJUA69zZJdQjkXxeV1fXzVGbaz1Jty0lnEix/aPL4qb46v2mY0zAZVG4/gxm7yI2vKyc6rKee1T0X8wJ85jkDUvbdufNWAppyVRbcsM0kiIhm2PAAAAAAAADgwoRx6S9MksqEnvWdlU059fNUZNuWUJ7STxUPvXR/uHl9VNeU0QzljNuWMGPA5Z3m2vE+PQV6Or5qdRyjnY98W8V3/TcSTVw+9JvffunatMuSXbdOUY3wVADDQo5ciPvdPDr0WANxb9WZXbwICAACGO6KzyBy73ZpyFi0i9aac+Tk35Sweei9+eXdTTra47p2mnBFPXGvKGUeeR6SL+/QYtmV2uxhfdSZNOVWA7RyDfntWDws2DQrldIyvqn8+SUJVOABwx9/9XyO+8WsOvRYA3FfGVwEAACMRyqG3NE1iPvT8flo25dTHV53hCfRmKOddH4548krE2290Xzebr57k3uaE98b1ucehnO/9nyJ+/Fv2dGP18VVHcKAmmy/HVw3ZX17+RMTf+sPj7mtTEsrZozVNOeXvk63uh45QTsXoKgCgw83j1deXAAAAAHCEhHLobZbG8PFV6UVxeaMpp7A40fyuLy8uX2sZYVUFZjTlDPIj3xTxM9+5n9vKs2Ub1DG8eyqf79aU8/P/b8QP/cWINz83+qpNYl17C+NaO77qdvWy1/K6frcdweMIADhueR7+ZgBgL47hDVgAAMDJEsqht1maxu3Q8VWzRVNOfXxVdo6hnMX2qzflRES82hbKKYMG2XShnHJZ9zGUk2f7C2nk9aacI9iW2bwIwiXpsANH1fecyEEnTTl7tC6UUwsS9l5c3v5xKUkiInEAFABokZ/Mn6sAnCCvQwEAgJEI5dDbLInIhoZyyhaRm0fLz53jCfRmKOf5DxaXb3727nWz2iiYlVDOiAcFqlDOPTzQ0GwYmvbGlvfpMWzLbB6RpsW/Idvg1PaLcj015Uyvaspp2dbV+Kpt7odN46sAADpoygFgbzzfAAAAwwnl0NssTWI+OJTT0pQzP8dQzuKkc7IYX/XwxeLy7ddarlsfX5Xf/fwY7vP4qjzb389VH191DNtyZXzVLvvLiRx0+lNdDAAAIABJREFU0pSzR2Uop2XfqAcJey+uowXszvJPZF8EAPYoP50QOQAnaEOzKwAAQE9COfSWJknMh74ITS+Ky5sny8+d5fiqRijn4iri8h0Rb7/efd1svhqsGHV81T0O5TS325TyWDblHEN4IJsXIaHk3JpyhHImVzXlrBlftU1TTr6pKSeJSPovDgA4I5pyAJjSqRwTAQAAjp5QDr3N0mSH8VVlKKc2vmp+hqGc8qBxUnvoPXyhPZSTlcGIeXebxK7ufVPOng6grDTlHMFBm5WmnCGhnDXBi2OU1x4rTKseFmwqQ1FbhaO6QjlH8DgCAI6cphwA9sXzDQAAMJxQDr3N0h2acmaLUE59fNU2bQr3RdWU0yOUU538zqYL5VRNOffw4EKe7XEfy4sQTHm7h5bNiyDcrk05hzro9NovRvy53xjxxmf7Xd/4qj1aE9jKRxxfVUqSiEju5+8oAGA3mnIAmJTxVQAAwDiEcuhtliYx37Uppz6y6qzHVzVCOU9ea7nufHlZP1k9ZtCkahg5giDJ2PJsfz9Xnh/p+KrZaY6veulnin+v/ny/669rb2Fcex9fBQDQ5Qj+7gbg/hLEAQAARiKUQ2+zZJdQzuXdz53j+KrqpHOy/NzGppwpx1cduBFlSnsN5dTHVx1BsCCfFyGhJB0YVDnw/rD1WLXF+mrK2YMeoZytxoh1hHIc/AQANsnD3wwA7InnGwAAYDihHHpLx2jKiViO+TnLppzF9uszvqp+grserBjzwHO+bfjhhOTZluGAnW6sNr5qTze5TtWUkw7bXw7dlFOGa/rul8ZX7U/VlNPy2Cq3/1bjqzY15SSLEVbH8MACAI6L8VUATMlzDAAAMA6hHHqbJUlkQ0/Sp+kyiHL1bHF5jqNmWsdXvdijKad+4nrE7bZ1I8kJaTYMTSnPF8GBiKM4aJPPiyBckgwcX7WmDWUfqnBHz33d+Ko9KveNlv28/N00OJRTX+YRPI4AgOOW55pyANgPTzcAAMAOhHLobbZLU07Esi3n8h3F5TmPr2prymkeUK7aSiYcX3Xfm3L2FdI4tvFV2W3R3JOkA0M5Bx5rNrgpRyhncuUusW581Tb3w6bfbWXYzQk3AOAOTTkATMgbRwAAgJEI5dDbLE1il0xOpJfF5dUilHPW46uS5ecevlCEY64frV63OsGdTRfKuc9NOXm2xxP59fFVR3CgJluEhNLZbqGcg42vKvfLnrdvfNX+VPvGSKGc+oHNzn016fg8AHDWNOUAMCnPMQAAwDiEcuhtliZxm+0Q3iibcsrxVfNzPIFehnIaTTkREW+/1rhqLWgwWVPOmhPsp67ZMDTpbdXGVx3DtsznxT6WpAPHneWNyz3buiknX/0+JlRu65b9aufxVVn75wEAWmnKAWBPvEYFAAB2IJRDb2mSxC6ZnJg1xledZVNOOb6q0ZQTUYywWrnufHlZP1k95oieQzeiTKX8eQYFUobcXrYIWiVxFCcGsnnRkjN4fFW5/Q4UMBLKOV7r9o3qfhtxfFUki9+XR/C4AgCOi6YcAKZkfBUAADASoRx6m6UR813mV5VNObOr4uNzPIFehXLamnKaoZyyKWe+epLb+KrN9t4AlEcRHkiP48RAPi8eYzuHck6lKae8v/cUwjpr60I5A5pyeo2vAgBooykHgCl5jgEAAMYhlENvszSJ+S4n6dPL4vLiQREYmJ9xU070aMrJOppyRh1fVd7GPTvQUIUD9hDSKLddsmj0OIZgQXYbkcyGh4Sqn+FQoZzavt9HPcDGtPo05WxzP3SNryrVW8UAAOo05QCwL55vAACAHQjl0FsxvmqXUM6suJxdFQGds2zKKQMctYfeMy8Wl51NOVkjlDPigQBNOSPcVv0+PZIxO1m24/iqA481G9qUc46/U/ZuTShnyP3QK3CYOAAKALTQlAPAhLwOBQAARiKUQ28XuzblzC6Xl7Mzb8pZGV+1IZSTzxttEiO2geT3NZSzz5+rvG+S4SGYseXzYl2SdGB7TN643DOhnOM1dlNO1/gqBz8BgE38vQDApPKOjwEAALYjlENvaZrEfL5LU85FcTl7cMZNOS2hnAfPF5dd46uyKcdXHXhM0VT22pRTu0+TI2n0yOYjNeUcKGAklHO81u0b1e+sbZpy8vaPK+X4qo7H1Usfj/jE9/S/PQDgHinDwkfw9zcA95vnGgAAYAdCOfQ2S3ZsyknLppyrIqCTacqJiKI16Oq5iCevtV83nxcnu8vvGTMokR04fDGVg4yvisV9dAQHavJ58RgbHMo58AmOKpTT9/YX1xvUCsR2+jTljDG+qnbfJ0l0+v/+t4i/+Yf73x4AcH8c+m9WAO43zy8AAMBIhHLobZYmMc92CeXMisuLq8X4qnNstSgDHI2TzA9faBlf1WjKKZuGxgwe3NfxVfWWocnVxlfFsTTl3EYkIzTlHGx81Zb7ZdWUI5QzuXxNAGrQ75OO8VWldYGciGJfP8eAJwAQRxGGB+BMeM4BAACGE8qhtzRNItupKaccX3W1GF91hidSq1aVxkPv4QsRb69pyqmHckZtyrmnoZx8TZvH6Ld1jOOrsiIEl852HF817mr1VjWu9AzZGF+1R+uacgbcD/V9rHNfXfO4yvP79/sLAOin+vvgCP7+BuAe2jRumYiIeOsLEbdPD70WAABw1IRy6G2W7NiUM7tcXs4uz/MEehXgaDblvHi3Kadqe8kWoZzL1WWMsj73NZRziPFVyfBmmrHli3FnSbq8j7dbwOLiQD9LNb6qb1NO2d5yhr9T9m1d4G3M8VXbjC5zcBQAzpTxVQBMyPNLP3/hn4/4h1936LUAAICjJpRDb0VTTkQ+9EVp1ZTzoPj4HMdX1VtV6srxVT//dyI+8b2r181ui49nUzbl3LMDDVXY6ADjq47hnbrZvHiMJemw+/bQ7zreOpSjKWd/ypNfa8ZXbTVGbMP4qo3frikHAM7Wof9mBeCMeK7p9OiliMcvH3otAADgqAnl0Nuf/e6PR0TEq48Hjp1aGV91cd7jq6LZlPNCxKMvRnzz74/43j++uO4exlfl9zWUs8+mnGMcX3VbjK5KkuFBh/rlvm07Vm2f9/e5W7dvbDt2rLmclfuv9vlkXdhNKAcAztah/2YF4J7z/NKPBlsAANhEKIet/Z8/+Klh31iGSi6uivFV83MM5axpynnzs0Uwp9wuVfvHlKGcA48pmkq17fY9vmpgCGZs+TwimQ0fp1V9z6k05RhftXet46sGNBZ1ja8qNUf93fn+PBwoBYBzpSkHgAmtvInEc00nr8sBAGAjoRy2djkbuNvMLheXVxHp5XmeQF8Xyqmu0xgBM2VTzraNJKdirz/XkY2vKoMR6awI5uwSyjlYU47xVUdrXStR1ZSzzf3Qc3xV576oKQcAzpamHAD2xnNNN005AACwiVAOvf2x3/4rIiLiq97//LAFpLPicnYVMbsoTtw+fiXi+//UfhpNjsGmUM7Fw9UwTkSxbSYfX3XPtv/BxlcNbKYZdX0W92nZlLPNKKHlQhYXB/pZhHKOV9VK1LJf5Y3fXdssb93HzXF/ze938A8AzpSmHAA4Dp6LAQBgHaEcevtVHyiCI5umiXRK6005F8WYpp/+9ojv+R8iXv7ZcVby2FUBjsZG/OW/LeI3/8cRH/ktd1temk05g0IWHe5rU059201+W83xVYduyln8zOkpj6/acr+sj3pjYmsCW0OacjaNr1oXyCm/59CPOQDgMDTlADAl46s281wMAAC9COXQ28VibNXtfOALrTJUUo2vuom4flR87vqtEdbwFJQBjsZD70s+GvEvf23RlNMcX5XNi3/l+K8pmnLu2zta9tmUUyqbcg69LctAxE6hnAMfVBnclCOUM7l8XSin8bur3wJrH3bc38m6sXDGVwHA+dKUA8C+eK5pJYwDAAC9COXQ28WsaCy4GTpqqgyVXFwVH89vl2GcMpxz33WNryqls1pLSHnyu9GUM+YJ6PvelLOPkEa17ZLi36G3ZXN81ZADJAdvyukI5Xzhpzp+nnKkkvFV0+sI5WTZ8mvbPO46m3JqDVRrv18oBwDO1qGD5ADcc55fNvNcDAAAfQjl0NtlumtTzqy4LMdXZbe1ppxzCeV0NOWU0otaUKbWOjFVKOcQjTL70Aw2TemYx1el6cARXkfYlPPyJyL+/FdHfPLv3L1+LpSzN11NOfX9bKvxVT2actYvIBwoBYBzpSkHgD059LGeY5V7LgYAgD6EcuhtlhaNBfOhTTnpoiln9mARPjnD8VUrrSotktny5HYVmJkXL3KFcvqrb7t93VaSDG+mmWR9dhlfdeD9oq3B6ekbxeXbr9+9ftWMJJQzva6mnIGhnK7xVSuPozVhN005AHC+NOUAMCXPLz14LgYAgD6EcujtshxfNbgpZxEqmV2e8fiqDSNZ0nR5cru6zIqTzuX4r0nGV92zF897DZWU224xvurQ7w4qAxHpCKGcQ4+vqgc9qvu0ZZ0OHSI6J13buh7E2eZ+qF+3dezVhvFVIZQDAOfLu/MBmFLe8TEVTTkAANCLUA69XcwW46uGNuWUoZJqfNXNGY6vKltMOh56bU052W3xuUmacloaSe6Dff5cK+OrjqAppz6+anAop7w8ovFV1Tq1/DyacvZn9PFV9Y/X7avrmnIc/AOAs+RvAAD2xXNOB005AADQh1AOvV2kuzblzBYLerBoyhHKuSO9qLXX1C7zbBnKaW2TGOi+N+WMua023VaSLopyDhxwKvebMcZXHbopp75frmvDEcrZo8V90nxsjTK+quNdiF3NYtVV71moEADoyYlAACbk+WUzTTkAANCLUA69XS6acl59dD1sAWnZlHNZfJzd1kI5b42whiegCnB0ja+arY6tiij+Xw/laMrZrKvNY5obW1wumnIOfSBipSlnNjCYdOATHFnLfrk2lFMGRYRyJtf12Boaymm7j+s2BnLyKEZYOQAIAGfHiUAAJuX5ZTMBWQAA6EMoh95ef3ITERF/4ts/NmwBZahk9mDRCHO7DOOcS1NO+WJ1m/FVZVPObIpQzpqgwylrtg1NqT6+KpLDb8vy9tOL4eO0qv3i0E059W255kDPPpuRzl5XKGdxn108XAYKey2upQ2p+flI1uyLDgACwPnydwAAHJSALAAA9CKUQ2/verZouvnolz43bAFlqGR2WXw8v4m4eVx87lxCORvHV6V3AyVZNl1TTnZPQzmdJ/cnvK0kLYI5hz4pUIYjkvQejK/q25QjlLM3XU055e+r2YMdxle17at9mnIaywEAzsOh/+4G4H5beROJ55x2ArIAANCHUA69PfegCIX867/+y4YtoGrKuVqMr7o54/FVWzblZNly/JfxVZvVG3Im/9ka46sOvS1XxlcNbO7JD3xQpQrl1O/HNSPJqlCO8VXT2zC+6uJq3PFVQ9cHADgfTgQCMIm842Mq3igDAAC9COXQW5oUjQXZ0NdZZSjn4kHRlpNnEU/fLD53Nk05tQBHm3KsV8TyJHc2n7Appww93LMXz/VtNHV7SnN81aG3ZRlkSWZn1JSTr34f0+kKR5XbfvZgu7FxXeOr6vtesqYtZ11YCwC435wIBGAvNjS4njVNOQAA0IdQDr2V50WzoS+03v2RiGffG/Hg+aLFIyLi6RvFZTnG6r6rAhxd46tmxcnlPF9tysmz5TbTlLPZzu0bA26rGhd16PFV9aacdLuAROXAQYesbb/UlHMUOsdXLf5fNuX0fhxsGF+1LpBT//5DP+4AgAPwdwAAE6q/CctzTTsBWQAA6OXi0CvA6bhIiyDJzXzgifpf8duLfxHLUUylsxtf1XGiOakFb/JGU04V+pigKee+HVyot+MMCqUMkQwfFzWm8udNL5Yhr62XUQbCTqQppzz4M3UrEtG5rcv77OLh4mrZ8vfZ2sV1BOia+17XvqgpBwDOlxOBAOzFEbQiHy0BWQAA6ENTDr3N0iRmaTI8lLOysGYo51zGV9VaVdosgk9FEKd8YdsI5YwZPFgbdDhhnSNxJrytJI2jOFCT3afxVfX7sUdTzt4CWB2evBbx2i8edh2m1jm+arHtZ1er/++7vLZlRkTxmFrXliOUAwDny4lAAKa0oe0aAVkAAOjJqwq2cjVL42Y+wgutelNOenmGoZyOk8zporwqny9PameL1px0VgQtRh1fdV9DOYcYX5Uc2fiqHZqV8gOf4FjXlNN2oOdYxld939dG/JXffdh1mFxXKKdsynmw+v++y4sYtr85AAgA58vfAQBMyfiqHgRkAQCgD6EctnI5S+L6doSQQ1oba/Lcl55PKGfTu2zKcS/ZbW181e0exldNHFz5yW+N+K7/btrbqKs3pkw+0qh24KHvgZof/WsRL39iotVpNOVEbH9w5NAnOLL56mVErG1EOZZQzpNXIp6+cdh1mFp931hpuWk25fS8LzoDdPXHVeP/qwto+V4A4Dw4EQjAPqxrbz1zhz5+BAAAJ0Ioh61cXaTjj68qQznZGZxU3Ti+qgzlzFdH8kwVyilPpE99IPtnvrMIouzLyon+iX+2+n2a9Bxf9a1/MOJHvnGa9amaci6W+9nWwaQDBx1am3LWnHSpQjkHHl91+/QMwiEd46bKbb9tU86m8VVdrWLN77/32x0AuMOJQAAmVX9jneeatWweAABYSyiHrVzORgrl1MdXPfe+iMgjbp/svtxjtymUUzbl5NlqW0ien3ZTTp6vttdMbSXMMfHt1uuMI+m3LbPb6QIkZRgirTflbHn/VoGwIxxfdcxNOfObwweDptbVbFP+3LMHd7+2dnkdoZze+553yAPA+fJ3AAATMr5qMwFZAADoRSiHrXz29bfjF195vPuCmk05Eecxwqo66dzR/rCpKSedqiln6lBOtt8mi3owYvLbLQ88JP0O1OT54nsmOmBRBb9GCOUcbHzVuqactlDO4muHDsTMr+9/Y0tniKZsytlyfFVX806lDLt17IvrGpQAgPvN8z8Ae2F8VTevyQEAoA+hHLb293/uld0Xkl4sP37ufcXl9Vu7L/fYVe+y2TS+6rbW/rFozZlkfNWa9pEx1Zt/9qGrzWPK2+p7/0w9bqcaX5XuEMo58EGVtgantU05ZSjn0E055zy+arHtLx6u/n/j4jaFcnquz73f7gDAXd6dD8CU6s3InmtaacoBAIBehHLY2ld8ybO7L6Q1lHNGTTkbx1d1NOUk6bjhlmxPY4ry7HDjq6YOAzXHV206EDHmaKjrxxE//e2N5S9+3jGaco5pfNW68EUVYDt0U87N/X93WP3nq2/vanzV1d2vrV1eV4CuuR03NeUI5QDA2Tl0kByA+616fjG+qpvnYgAA6EMoh6386g+9GB969zt2X9DZjq/q25QzX57UzhqhnCnGV039jpY82+8L9H025ayMr0p7jK8aMfDysW+L+KbfE/HGLy0/VzXl1EM524ZVDvxOp9bxVeuacspQzqGbcq73Gz47iK6mnHJ81YPF/0caX5UsxsJtsxwA4Ex4dz4Ae9B1DA9NOQAA0NNkryqSJPlQkiTfmyTJTyVJ8hNJkvzhqW6L/blMk7idjxByqDflPFuGcs5hfFXZlNNxknmlKWde+7gM5czGDZm0jQmaRH7A8VVTN+XUx1clPcZXjbjNbx4Xl7dP7y4/mS1DXqfUlJPnHaGcPk05xxDKueeNLV3jpvKhTTn55o/7fP993+4AwF2acgCYVPnGutrHNHguBgCAPqaM+t9GxH+e5/k/HRFfHRH/aZIkXzXh7bEHl7M0bucjvNAqm3IuHkY8fL74+ByaclbmUbeomnKy1ZE8UzflTH1CO8/2e9K8HgiYfDTXwPFVYxzQaQtVtTblbHlbY67jtrpajvo05Ry6peZ2h1DOJ/9uxJ/9dcVIsqPW1ZSzCESVTTl974uNrVaLx1XnPiyUAwDny7vzAZhQ9fTSp731TGnKAQCAXiYL5eR5/tk8z3948fGbEfFTEfHBqW6P/bi8SON6zKacq2eLfxHnEcqpt6q0qUI5t6vjqyJfhixOsSknz6YJTLz1xYiXPt5+e20fT6I5vmpTU86acMnWN93SaFMfkVaFcrZtyjngO53qbTcr670ufJHf/d5DmF8Pb4R66acjXvlExOOXx12nsXU9tsqfezby+KqN3+5deQBwtvwdAMA+9BlVfrY8FwMAQB97GYqbJMmHI+LXRsQ/aPnaf5gkyQ8mSfKDX/ziF/exOuzgMk3iZpRQzqIp5+rZiEuhnEp9fFUVNLhZfk86dlPOiAGRtbeTT3Mb3/3fRvy1f7vl9lrCAlNZCcGsa/Qorz/iNm8LVVXjq9JlI9PgUM4B2ke6QjnVdmv5nnqr1CHNb4Zvs2MZwbVJffu3NuVcrf5/4/K6Qjl9D+hpygGA8+Xd+QBMqd6M7LmmlaYcAADoZfJQTpIkz0XEt0TEH8nz/I3m1/M8/7o8z39Dnue/4b3vfe/Uq8OOxhtfVTblPHeeTTld1bdlU878Zvm5+fXiW3o2sQxZn3005UwRmHj9MxFP7/xaWW3l2UfgKGJ5/2xSBWlGeBy1jR9rG1+19bY/4EGVzlDOmvDFsQRa5k93COWUIbwDB4s26gjRlB9fPCwus57bYVOrVZKsPwB6yAAZAHBY1Z+sTgQCMCXjq7ppygEAgD4uplx4kiSXUQRyvjHP8/9ryttiPy5mUzTlPBMRyZmEcmqtKm2SllBOvV0nScc9aT9mQGSdPI+IvLhMRjyY8filZWhp5fZammOGevNzEd/8ByIePBfx0d8a8Zv+g+aNFRdJEhFJj/FVI4YIyhBKa1POrNa8tG1TTstYrH2p79/1j08ilHMdg/fzKpRz7E05tX1i5b5arPdsy6aczpDPlk053pUHAGfI8z8AE6q/CUvopJ2mHAAA6GWyUE6SJElEfH1E/FSe5396qtthv65madz0bUBYZ1YL5SRJ0ZhzFqGcDeOr0sVDsgyapBfLk9tlKGfUppyWppUp1Bt5yqDIGB69HDFvOfnfORJngM//RMQv/EDExTMRn/oHd0M59fajJNncEFJdf4QDFm3jq9qacoaGco6qKWdNq9OxtMyUYbpB+/mJhHK6QjTltr94sPj/ruOrSsnd67V9vwOkAHB+/B0AwKTK5xfjq7p5LgYAgD6mHF/1z0XE74uI35IkyY8s/v32CW+PPbiYJXFzO8ILrbQ2viqiCOdcv7X7co/dxlDO4vNlKKdsnYhYNp+MGaCpQgz5tC+gqyaTEUMTeR7x+OX2ppyutpVBt7NY9/d9VXvgZmV8VZ+mnBFHhrWFqqp9bJdQzgFHAq2Ecur7ZI+mnMj7j02aQrkvDtlux9L2s0lXiKbcF2fbhnI2jK+KiPVV4cZXAcD58u58APagz6jyc6UpBwAAepmsKSfP8x8IQ3fvnctZOtL4qjKU8+zy8iyacuqjjlqU7RrlCe1yzFfEtE055bqNOVpq5XZGDKKUrt+KmD9d7ktttxexe9ioXFZ62bH+9ZFkPd49NeZoqKxluzbHnTW/3sdBx1ft0JRTfn96dfc6U8uy9nFifU0RXJtEV1PO4me/WGz7IWPjBm0378oDgLPl7wAApmR8VQ+eiwEAoA9Rf7YyWiinPr6qvDyHUM5KgKNFugjlVE05U4dyRhzztPZ2ykDFiIGDRy8Vl9nt3Rf/K2GjHX+uMiQx6wjlrIyvSjcfiKjabUY4YNHWlDPG+KpDvuu4M5TTpyknDtc0U29s2iVccvRNOR3NNmVArGrK6flYX/kd1LK/lQ1UnfuiphwAOF/enQ/AlOrjq2ilKQcAAHoRymErl7MkbuZjjK9qhnKeM74qYtmUs69QzsqJ8ylfQE9w4vzxy8uP5zeNm2sZqzNU1ZRzsb6l5RDjq6pmlnqwYfHzJuky5HVSTTkdgaq1TTlHFsoZ0nZz6uOrqqackcZX9d33DjlqDQA4LO/KB2Af1r5R5NxpygEAgD6EctjK5SyN22yEk59lWODyzJpyqlBOx7tsylFMZcikGcpJpxxfNXC5N29H/KlfHvGxv73mdiYYzbMSyrle/VrXif4hym2UXrQHfFaCVluMrxrjgE62qSknufv1Pg75TqeVUE5LaGxTKGfMNqZt7NyUcyKhnK7xVeV2v9iyKadreZVN70gUygGA8+VEIAATWnlTyuFW46h5DgYAgF6EctjKxSyNm3ke+a4vui7fUbTjvPDB4v9nFcpZc5K5Ob4qrYVy0ombcoYu9/qtiLc+H/HKz3VfZ4o2i3J8VcTdUE7959o1CFSuc9f4qnqdcZ/7Z8ymnLbxVVVIaIfxVQdtyukaX7VmndpCSfu2ayin3I+ym/VXO7RNTTmzgU05yWzNdku690VV2QBwvvwdAMCk8iiO4RlftZFwDgAArHVx6BXgtHzfT38hIiL+8adei1/3y941fEGXDyP+0D+OeObdxf+vnjuTUE7ePboqYvm1qinnavVrSTpu6GCMppxyfdadhB8ziFJ6XAvlNG+7KzgwRFZvyukzvmrDgYhsxMBL1rJd6+Oryv1p230mP+C7jjtDORuactLLItByDOOrdmrKOVCoqLfaPtEWfrtY/M7qHcpZLC9thnL67nuacgDgfGnKAWAPjK/qJiALAAC9aMphKz/26dcjIuKHf+HV3Rf23JdGzBa5sKtni8aV+y7P1odyqqacjvFVa9skBhijKacMgcx7hHLGDBysa8oZdXzV4sBCV1NOfXxVkkbv8VVTNeWUQZ10h6acOOBBlZV9si1c1bZO+fKxcrBQTq3h5j6Pr+psylncb2VTTu9tUIZyOkJv1ai/rqacCQJ/AMBpcCIQgCnleW0suOeadgKyAADQh1AOg9xmI7/YOqfxVWtDOYuQUhkyuRPKGXl81RjhlV5NORO0WTx+ZfnxvDHyZ6UBaNfxVbWmnIiWAw218VWR7Hd8VdY2vqpsypkV/4bc1lGPr+poK6pCOQdqmrl9WlufIaGcxeWxh3Lm/e0sAAAgAElEQVSiI5RT7nfbhqP6jK9K1lSFV+fiHAAEgPPjRCAA+2B8VScBWQAA6EUoh0Fu5yO3Ely9oxg9c3u9+bqnLM/Wn2AuQxRVKKdlfNVkoZyBL6DLk/HZzZrrZKvXHUN9fNWdUM4IP1dzWWUopxn6qDI55fiqnssb44BFGXxoayxJ0+FNOYc8qNIVytn0udmWY5PGNtr4qiMP5bQFcSKK/S6Z1R4n246vSgc+bp2MA4Cz5UQgAJPKIyJZZHI817TzmhwAAPoQymEr/+4/++GIiHj/C8+Mu+Cr54rLez/CKu83vqo8ob3SlDMbP5QzxviqchnNYEzdFCNm+o6v2rU5JWs25TR+hub4qn025bSNr8qzZbirCuVseXDkkCOBqn3/qn3/3BjKOVBTTn3/H7QO+Q7fu0cr46tqH2e3xWNk21DOyviqrv006f7aFC1cAMCJcCIQgAnVx1fRTkAWAAB6EcphK1/zGz4UERHPPpiNu+CrZ4vLm8fjLvfY5BtCOeXXylE4zaacZpvEzutTDz0MfAFdja9aEyaoWkBGbsopw1zNlp4xwkbN7y8DUneWVx9fVf9/1/LKIM0YTTktQZV8vtyPqlDOltv9GMZXza7am1M6Qzlbjk0a23zX8VUn0pRT19zv0ll3o9SmZdwZX7VlU44DgABwfpwIBGAv1rxR5OwJyAIAQB9COWzl6qLYZZ7ejj2+ahHKuX407nKPTZ71a8opm1/SelPOBOOrxgivNMdXPX0r4smrjetM0Lzy+JWId/5Txcdrx1ftGsopm3I6QjnlgYckWdw/m0I5UzTl1BtL5sv9qHxH19a3dcjxVYufaXbZfj+2hnLy5f1jfNW0VppyGo1UyazW9tU3lFM25TRDOQub3pWoKQcAzpgTgQBMaTG+qvqYOwRkAQCgF6EctnI1K3aZ69FDOWcyvirPYvmCvkVz9MvK+KpF6GPMtpl8xPFV5Tp/x38Z8X/8vuYN7XYbTbdPI56+EfHO9xf/XxvK2XF7lcsqwwbN5VVNH0nxr+/4qjFkG8ZXVeu85W0eRVPOg/bmlE1NObve30PV98FBoZxyfNWRh3KiEQCrf5zO7o7g67u89KJ7uyVJdB/gE8oBgLOV3/kAAMZTjq8ywmoNAVkAAOhDKIetXF4UL0Rvs5FfbJ1NU86GedRJoymnOb5q9KacERplypPvZSjhrS9EPHpp9Tpjj696/HJxWYVyrle/PmZTTrmNeo2vWhceKK8+Yoig3PbNxpKqKSe9+/U+Dtk+0jm+ak1QaGV81YFCObdjja860Pr31dmUc7s6vqpvOKpqmmr8but7QC93ABAAzpe/A2jxie8pXpMCjMb4qk6acgAAoBehHLZykRa7zHf95OfHXfDZhHL6jq9aBFzqTTnprAjtjBmUaGsi2XoZZVPO4nJ+09ImM3LIowz9PL8I5TRbOZohlV1UTTmbxlf1DE21tdsMXre2ppz5ch8bHMopr3/IppzLRgBkU1PO1er379vOTTknMr4qOkI5+bwI5CRbNuVsGl9VNot1HgDVlAMAZ8uJQNr81X8j4of+4qHXArgXjK/aTEAWAAD6EMphK2XJy3d/bOR3nlXjq848lJOsCeVM0ZRTD3AMbsopwwSLdc5u7wZhqpaTsZpyFqGcrqac+u3vemCgXOeqKaexvDvjqzY15Yw4GqpzfFUjlLNtMOmQ7SPlus6u2sertYai8iMI5dT2wUGPpRMZX7WuKSepNeX03ue6xlfV97017WKHbHUCAA4sX7mAiCj+Lm++PgQYwviqzQ75pi4AADghQjls5UueexAREb/jV39g3AVXTTlvjbvcY5Nn61/Mp43xVenEoZxs3t0A01dea8gpL7uaa8Za97ffKC7f8SWL25xwfFXVlFM2gDTDBrXxVUkaGw9EjLkt2pY1xviqOOC7jleaclpCGl1NRVUY5AhCOUPamU6lKafZjlPKsuI+6HycbFheVwvYxoOfQjkAcLa8K58mo02BSRhf1cnvXQAA6EUoh61dXaRxdTHyrmN8VaEZyinbPyKK70snaMqZ7RjKKU++l2GC7KY7OLHrKKnqNhe3dflMcTlfM75q13aecp07x1eVoYI0igM1G7bjmO8iam3KmS8bl8rLbQ+OjNnms60qlHPVHq66s06L/5f78Vj72LZ2bcopf67mvnx0WkaKRSzGV6WLdxHOth9ftS5wmCTR+XgxtgIAzpi/A2jQ2ACMqj6+inaeiwEAoA+hHLZ2fZvFN//Qp8dd6OWZhHIi7ze+qgomXNS+NkVTTrZsGBkcyrldvZzfrBlfNdKL9HJ5VSin2ZTTMvZo19vqCi9VoYJtx1eN0ZTTEsrJst2bcg46vmqxH1086AjldISiqvFVJx7KOfqmnDXjq8rfJekWoZzyd2KSNpbdd9/TlAMAZ8u782myTwBTWPdGkXPn9y4AAPQilMNxmF1EXDw8k/FVPZpybp8WlytNObPie8cMHeTzWihn4AvovNmUc3u3naYKVEzUlJPdtN9exO7bq1znzvBSY3zVxlBOS5BmqHI7dDblJMNu65DvMO0aX1W9+aorlFM25RzB+KpBoZwTGV8VXaGc2n6XXmzRlJNF8djpapna8K5EBwAB4Ix5dz5NAtvAiPK8dlzFc0072wUAAPoQyuF4XL7j/jfl5Buqb5N146uS8Zty8qzWADPwhXS2WJ/5zfKyqyln7PFVFw9Xb7u6vXz3BqD6siK6l1cfvxM93j21a2vQt/3RiP/7vyg+rsZXNYISaVpbp9g+DHXQ8VWLde0cX9URykkPHMq5rYdyBuznpxLKqe8T9cdzdrsMFaYX/R935UHOO7/b6vveugYqoRwAOFvCuTQZbQqMLgkjrNaofu36vQsAAOtcbL4KrPrACw/jC28+HX/BV8+dQSinZ1NOvS2klKRFaGfU8VXz3cMrVVPO4nJ+fTdYMObIpohaU847lre58vV5EdLIbne/zSokUoaXmoGj+viqHqGpfMd3b372R5ZhpLbtms2X+9jQ8VWHfNdxte9fLQNf9XXZOL7qGJpyhmy3Exlf1dWUkzfGpg0aX9WynyZ9m3K8GxoAzo8ABk2CWsAEjK9aw3MxAAD0oSmHrX31V74n3v/iw/EXfPXsGYyvytefZC6DHa1NOWtOXA9enxFCOdXYqpvl/7tGPI02vmqxnMuuppx6A9CuTTmL7y9bjNaOr4oe46t2XJ/bp7Vt3jIKqz6+Ku1a5w3GDlFto3N8VUd7T3N81aHCGaONr6o9Rn7pRyL+l18R8dYXdlu3MTVbmUrZ7Y7jq4b+bhPKAYCzpSmHJk05wJiMr9rMczEAAPQilMPWri7SuL6d4ATo1bOaciKKE9tlyCRtNuWMHMrJ5ruHGaqGnHXjq0Y+cV6FcsqmnGYoZz48kNJUhlyq1pmOUEhSVhr3HV81cL1u314GHvKWUE42X20sGXJbhzyoUm/KWQnldDXlLD4/O/D4qvo+OCiU09KU8+l/FPHmZyNe+pnd1m1MbUGpiNXWrfSi/6i6vKMpp77vrQsy5nc+AADOhgAGTU4OA2MqR9AbX9XNczEAAPQhlMPWLmdp3MwneLF19WzE9ePxl3tM8mzzOJa0Fsqpj69KZ+OGcvI8IvIRx1fVGnPujHhqaQHZRXlbFw+Wt9m8vTLQtOttlkGqrpDPtuOr2tpttnHz9oamnGzZWDI4lFNe/xChnHJcWDOU0xFmOprxVbWRfkP2ueoxUlv/1z9dXD764vD1Gt2appy01tDUO5ST1R47bd+z6eCnphwAOFvenU+TvwmBKRhf1c1zMQAA9CKUw9ama8p5TlNORBGSqcZXNZpy0hFDOeVJ8zKUM/QAQ9YI5cxvW5pyOkYPDVXeVnpRhG/qo4PK2xtzfFU6W4ap7oQNylBOWlyn/jPObyL+3G+M+OlvX11exPBtcfv23W3eDK+k6XKdIiKyU2vKSYr7duW+62rKWfy/3I9PtimnJZTzxmeKy0cvDV+vsdV3iTv7XdmUM9vyfihDOSsLX/161744dgsXAHBCvDufBieHgTFtGkFPeC4GAIB+hHLY2uUsjev5VOOr3hp/uUcl3258Vdn+ETH++KqqYWTH8EozTJDd3D0hXx0cHakpJ68FimZXd8dX1Uc47Xqb2Xy57SO6QyHR0pRz87gYPfTFn757/cHjq57eDeM0xwgljVDO1rd1wIMq2W1xv94ZZ9TVlFOOrzp0U04tGDbovi3HV9X211Nryin3t/Si/+Oua3xVaeMBUCdeAOBsCWBwh5PDwJhq46s817TzXAwAAL0I5bC1q1kSN/Ms8rFfcF09qyknomg56WrKSdLxRkDljaacoSGRqiHnpnaZr74g3zWIcuc266Gci7uhnDxfjq/auSknL4JSnaGc2viqaFQat7Wf7Doa6nbT+Kr5eOOrDtE+shLKqe3rXY0od8ZXHagx5bY2vmpIEKx1fFXZlHNEoZz647r+uyib15pyLvqHo6rxVcnAhiFNOQBwvgQwaHByGJiM3yvtPBcDAEAfQjls7XKWRp5HzDOhnK3lWRTBjTWS2TKUk9ZDOUnxtdGDLTuGV6pRSvPi4Gc+X/18fdljBYrKE/5JumjKmXJ8VdmUUzbvNJe3ZnxV1hJu2SXwks1Xm4jyllBOvSVocCjnkOOr5uubcpoHepqNTyc7vqpsyqkFrt78peLjYwrlVO8UjLthsGq/22Z81WJ5d+7v2v2cNMJud74/dn+cAwCnpzoP6EQgJSeHgRHl+aIoxwirTsKQAADQi1AOW7u8KHab0UdYXT0XcfPocE0X+1COalkn3df4qkWgY1Y25dReQL/6CxFvv77dcrKbRjChJZQz1viq7HbRXpMUoaKs2ZRTa+3YdX/Ks6K9qAq4zO9+PSLagwVrmnKGHLAo21iyRvBppZVoxKacg42vWjQT9WpbKsdXHTqUcx3LsMqA7VaFchb78lufX/4sj17aefVGk+ft7VrlYzJi0ZSzzfiqlsdOZcPBTwcAAeCMCWDQ4G9DYFTGV23muRgAAPoQymFrl7Nit7mZT9CUExFx87j7Om9+LuJnv2vc293G7XXEZ39s+Pf3CuVcLE/Ml4GZiOUIpSlGQJXrVvrLvzPi+//kdsuZ36yGY9qacsZc93K9Z5ct46uy3cdy1W8rSbsDLr3GV420LW7fXizvdvWy2TBypyln2zDUIZtyFuOr0o5WqOY6lddJjyCUc/FwdZ220dxXytFVV88dWSgnW+5fK6Gc2mMunW0Rysk6QjnN+3lDU44DgABwfpwg5Q5/GwLslTAkAAD0IpTD1i5nRXPBzehNOYtQzroRVv/w6yK+6d8a93a38ZPfGvF1/2LEWwPHyZQnoNepj6+atCmnEWaoL/fJqxFPXuu3nKq15XY1HLMSjshXr7urMrgRsRhftS6Us+Nt5tkyEBXRHQqp7p9Ga025vs3rDwrllE05jTBOc3xVua6n0JTz6KWI7/yvIua1oFF6sRgF1mPs153xVSPtY9uaX0dcPBi+Ds1WpTc+XVy+/1cf3/iqapRb7efMbotGqYhFKGfL8VVdIaxNvy+rA4D3uGENAOjgRCANTg4DY6qaXY2v6iYMCQAAfQjlsLWLxYnX29Gbcp4rLq/f6r7Ok1cj5k+XJ/D37e3XipO/T14Z9v19QjlpLXjTDOWkLaGc+U3E931txNtvbL8uEbXxVY1gR9+T3PXgyUr4pN4OM/LB0Wy+bOuYXS5DTM2vjxFiyufLbR/RPT4pFgdqWkcutTTlDDlgcfukuKyacsrxVfXwSi2U09Zo0scuI7a29Ynvifh7fy7i5Y8X/y9bkO6MAusIXzRDOWONSNvW/Dri8pnVddpKGVxb3LdlU877f03xe+f2uv3b9q3exNQMoFVNORf9Qzlle9jgx6pQDgCcrdyJQBrsE8AkjK/qJAwJAAC9COWwtemact5RXK5rynm6COzMn457232VIYina4JD6+TZ5vFVZQtFxDJoENF94voX/17E9/2JiJ///u3W5c74qnoo57Z/20dnU05by8mYTTn1UE5LU85Y477KcT19xlclaawcAK62TXb3c0MOWJRNOWUoLW8J5dQDS13tPpvs86BKNZKrth+V27vP2K+q8WkR5DnY+KqbkcZXlaGcT0dcPhvxno8U/3/88u7rOIq8Y3zV7fJ3V3qxRaivY3xVfd9LGmPhVr7fAUAAOF/+DqDJPgGMadHsyhrCkAAA0IdQDlu7nBW7zUHGV5UtOreHCuXcrq7H1vLNoZy0HsppGV/VDMu8+snisgw39F6VMpTTMr4qu+0foCmvN7+JyG7ufr6+7NFGb9VbOS5Xb7e8na7tta2stqyIu8urQjktoZ1m0KL+uUGhnDLA0mzKqW/r+TIccQrjq24WP1O9camtKaerEaU+Pmybhpax3T7dMZTTGPH2xqcjXvhgxHNfWvz/WEZY5XlHkK/+mBwwvqo5+q2y6QCophwAOFtaUWiyTwBjqsZXRfi90sEbZQAAoBehHLZWhnJus6nGV61rynmzuDzVUE6fppzyxHbEMjAT0T2OqQzlNEc4bVKe/J91hHK2bcrJ542mnJZQzq4Bmfr6ldtpdtXRlJMU4ZQxmnLqoZyxxlcNWa9yv8/6NuUky89t5RBNObfLy9bxVV1NOfXtv00YZGTz64iLB4t1GrCf32nK+UzE8x+MePa9xf+PJZQT+TL0Vd+vVva7Le6HanxV0h7C6vP9EUI5AHCW8pULENgGJiN00kEYEgAA+hDKYWsXi/FV17dTNeWsCbyUoZyDja9anGjeZXzVpuaHzvFVHSGTqilny21SNeWUt1cLYuTz/sGC+on5myd3l18us/m5XWS1NpjZRXsopyvE1PT3/0LE9/+p7q/n80VooGVkT8Td8VW9m3KGhHK6mnIaY3/KANGQppy2UNGUqqBRsylnFhH53XdddW7/sinnQAfg5zcRl8+srtM22sZXvfBltVDOS7uv4xjyjvFV5eMkYnE/9G3a6hhfVUqSiEjWbFMHAAHgbGlFoUljAzCqcnyVEVadcgFZAADoQyiHrV0uQjnjN+X0GF9VNeVs2QozlvJE8/Wbw76/V1NO7esroZyyrSVfPcg4uClncQK8Ob5q21abvCOUM1YQpU12uwwGzK7u/uzZIiCQ9mjK+ZnviPjY3+7+ej3gU/6/+fWIZXigfiSiCpq0NOUMOWJRBa/y4v6rQjn1IM18x1DOgLaSXVRBo9q2WtnejTDOuu2/1dikkc2vdxtfVW7rsqXq0Rcinv9AxLNfUnz+WJpyysdD+XFpZXzVNmPE6uOrdhj75d3QAHB+dhkLyz0lqAWMqBpf1TjWQ43fuwAA0IdQDlsrx1fdzMduyukxvqps0Tl0U866dVyn3mLSZaUp56r2+Y4RSoObcspQzsXq/6vRSD3v35WmnMftn596fFXWNr6qbSROizxb3+BTBnyqbd+8bn18UtreNNO2LXZpyokotsHG8VUd7T7rrKz/gcdXRdwNMXWGctItwyAjq4dyhuzn5bbO5sttcvlMxIPni338WEI59fFVzZF35X6XzrYI9cXy8dVsfLpzxa4FxLDHEwBw4pwIpEFTDsB+VU/FXpMDAMA6Qjls7SKdKpSzaMq5WdeUswjlbBtAGcvO46t6hHLKsEnEssUmYhE6aAQV3n4j4vHLxcfbBpXKQEfZxpPXmjoiuk+qzxuhh3pIpR4aaRvjNNbB0WYrR9v4qmTRtrIpHJBn60ce1ZcVcfdnqI9PaoaAqtBMWyhnwLa4aYRyWsdX1UZ7dQaJ1th7U85ivy3XsQrlJKufr7Zb4/uPKZRzuUNTTj0UV26T2YNiOzz73iMdX1Xfr+uPyS0aizaNr4pkuS+0fn9tvQCA8yKAQdMuraQAd9TGV3mu6eC5GAAA+hDKYWuv///svWusNFta3/dUVXfvvd/9Xs45c87MwMAMw3AZLrGJwBgS7AjkxES2nARLJjgWSYxkidj+gIHcnNhRgp0QUKRYfLCMIwtsQEZRcBJmPBjEwDBc7Mlw9TAzwJyZM/fDOec9573tW3dV5cOqZ61nPfWsunRX79699/8nvep9qa5aVV3d7+5a//79T11V0I/+ygvTrnh26CZmUxaaqgyBnV2Hci7WDeVU3RPMRGHCm0jVVxkVSq+Jx0AHU/rgQEfSlGMEOU7uE/0vn0/00V8S6xGT7ylTjn+TviVTjq6v4gqnbEB9VZ8px1t31qiv2rkppxnz6WtEP/Wd7vHr5bJNOU3lWZ8pp7e+Kh9naJmaTeurZCiOz+dZY8o6fvYKmXJIvGaoqrZMGJoGP9c3rK/CBUAAAADgBgNTDtDU0Q0AAGwE6qv6qfF/MQAAAAAAAENAKAeM5pOvukn0d33gs9OuOMtchVUqlCODMDurryrbYxnDkFCOrK+S1hwrGMLVVURr1FfpUI6oz5G3kpNXXPBGhoGi+ippyuHAiBFS2ZSqDNagYm7Ye2pRiTPElNMTyskNS1FYoLk1ggU66BTdf40LFvIxrlZ2UIXNPkThfHn+3US/9eNEn/71/m1Y4ydyppZ3fDfR6qJ9n03gfapEgCk3AmhJJTKbijJ3v6mCX2Mom8dio1BOwpRDRHTrCoVy6lR9VblmfVXKlCMe165PJfpPyEOVDQAAANw4YMoBGkwOAwDAJYP/iwEAAAAAABgCQjlgNF/2OXe3t/LFcTrwIiujpg4GDGXT+ioaUl8lfp8XccCC78sT3q824ZisaNti+uB1+PqqAaYctvFIK4+cDI9MOav276eymESmnLlhyuEgzUBTTlfVTlXax97fX9VXRfflQIm4j1U5NRRpypEBnVYoJw9jIiJ6+Q/ssVuk6qs+9l6i9/1Dopc/PGrIvfA+aVNOroIfg0w5O6qv8mYbDuWscZ7zfpTSlMOhnNeFmrqdw69hWXw+VSthaBoRjvKfPMzXvIiHUA4AAABwc0EAA2gwOQwAmBLUV/WCMCQAAAAAAACDmPUvAkDM13zB09tb+eI4bco5fxS+luGEy2SS+qqeUI405XAlTynqmHg9RM6Uc3iPKJ+vYcpp1pGqr6qMSW4ZnPA/E5PvUb2SET6ZymJSl3F9VaWqu2SQZpL6KsPcIn9PFIIFRCFo0FlftaEpp0yEcqRFiG91RVQXkdmop4prCrwpp6e+KhW+uEqhnPkGphzev8iU09RXFfOrEzqp6+aapLZCiedkXtivH/YKQ6AtWt/Qu29gngIAAADA9QATpYDB5DAAYGvgdcUGYUgAAAAAAACGAFMOGM2i2OJp0xXKieqrdmzKSY0xxU//DaL3/0gzgdxTXxVVVhXh+yj0IUI5T3+BM2qMrfTqC+VYQRX+nTz+cjlpyklVK02BNOXk89jcw9vJGsuQDAesztuWpbrqDhDUypTTVV/Fj63fd6MKbKxmf3lG9BN/0dluIlOOfAxUOEIHu5hBoZyEKSdlqtkU3id/rBKhnClMOVVF9PwvTH+xqGXKWWP98vnHz2U25ejAyi7h1zAZyqnr8JwjGjdea30RWWOgQn0VAAAAAASY/AMmmBwGAEyIN7v2XMe7ySAMCQAAAAAAwCAQygGjybb5ZnTeZcp5GL4ea4WZCg5XSGtP730qot/8MaKP/uIwU04uAxVZmOjODVvLqx8jeuotzqTBIY0/+DmiT7xvwLh66qssI4oP5SzbPyMiWp621x9ZXKYK5YjgSWGFcjhIo8IBP/ntRO/4rva6+kw5skasKxTCzw0dFKgtU87AY/HwU0QffgfRC++NQzmRKUdc/KhKdQ6NDOVYQRy5jclDOWzKEQEmea5XIvhhbT+qDyu6TT4vvJfoR/8Dohc/MM3YmVYoZ41jxPtRrcJzmU05WT69oWhtZN2UsidtXF+1gWEIoRwAAADgZhGFLjARCBowOQwAmBTUV/WDMCQAAAAAAABDQCgHXC0Wx+lqqHNpytlVKGeN+qpHn3ZhitV5MwE9sL6KQx5cQWTZWs4fEt16hqgQppx/8beI3vu/9Y+LJ81zHcop499LfGBHhnKkKUeERvj+VgXSplSrEAAo5m1zEoefsjzejwefInrtE+1luwIPVY8px4tyxKen9LGUYRg/noEXLHhdZw8HmnKEsYRIhXIGBCVSFUK1EbKaAg5yVRuacihrapM69vGiMTmNNV31oUM56wRo5DnTMuWsG1jZAnVzUTIXwZtahXL6Hod4hYmqOfX8SF3gG2ueAgAAAMA1IVG5Cm44+NsQAAAuFYQhAQAAAAAAGARCOeBqMbS+amemnCZccT4ilPPKR9zt6nygKUeEcIjikE6uggrlhbNpzBYhpLF8Muz4aLsFCVOH/H10H8OUIyfSZX2Vr8FS1UpTUJWhvqpYuPVWKpiSF01wQJp6VnGwhaip3uky5dRNFdaI+ipt74jqq0aacmQAKwrlnLWXIQqWIGZ0fVXiU8fWvkyBN+Wswm0+awecUhfYvSkn66+vssxFU8DPh/kmphxZX9Wsr+BQTrHeOofykXcTPfzMwIVliEa9ZmTrmHJ66qsy+bxKjMevBwAAAAA3BphygAUmhwEAU1KT+AAWXldsEIYEAAAAAABgCAjlgLX40jfcobc9dzz9ihe3O+qrRGXUrkM5Y0wb95tQTsmmnJ76Lw6byNoqItvWUi5dKEWaclbnbXOMBU+a+/oqNcHeZcopE6YcGRSx6qsmNeVwKKcZf2TvqWz7RrU0Qjk9ppy6tC048v5E6vGpw32JNgzlNOs6exif91F9laoIi+qrxNdjQzmW5Wjy+qrm8ai1KUfVhfWZcrJ8RChnSxVcm9RXMdVKrE/UV20zdPKT3070Sz84bFmrboqPOT8n82J4VV2qvmroBb1t1aoBAAAA4IoDUw4wgEURADApqK/qBWFIAAAAAAAABoFQDliLN947pNuH8+lX3FlfJUI5Q0In24Ann5dPhk86jzXltMI4RiiHQx7lhQulzA6EKec0Ds0k96UZf6u+irSrPzcAACAASURBVEM5xhvqkkM54vjLEIQ05Vh1R1NZVqpSVOXM22Oqq2aiX9XoyMCDXLbTlCOsO/x99HthatHBHcvM4n82sr6qZcpJ1VdtaspJVAhtyzLTMuWUifqqRPiiFcrpCVjxNqaEn28+lLNJfdUqBK4KWV818Zgly1OiP/zgwIWbi5JZJsKBzWPHAbkxppxkfRXT96lEXAAEAAAAbiQw5QAT/G0IAACXC8KQAAAAAAAADAGhHLAWRZ5ROTSUMgaur7LezF08dgGMfLZDU46YaE6FhzT3n3e3qzNyE9B9phwRwom+L2J7SF03oZwDZ8vxppyzgaYcDuXM4u+rjuCCrBjy6xHLLaUpx7CSTGrKaY5F0dhEdKUWV07JbZYrF0CQ1GV3SKMqbUtRWIG7yaz6KmUfkvcfW1+lTTmp+ioZWCJy4779hvC7/g3a691afdVZvF5+bFuhHFVjpcfF9W47qa9qnm+bmHLk/vHzKDLlbPECV126UM6QbXizTRE/ZkTClJMPP09qDvmsuY/4NDQAAABwQ4EpBxjgb0MAwJT497+or0oCUw4AAAAAAACDQCgHrEWRZ7Qqt/CGa3HcmCKMUMn5I6KD227ie9f1VUTDK6x8KOdinCmndauCIZWwU8wOGhNP3YRymoDKJ/8/oh//1mCykPj6Kh3K2aC+yjLlWNvclLoU9VWz9pjq0rZvpEw5nXYVEfAhai9bV+TDODpIYgWc/NcjTTlnD+IgjjYD+a/LuLIqy4iee3uz7ZGmHHkx26ojmwJvytH1VXws1XZ768M6jqt/XCbeBw7EzTcJ5Yhx8/OITTl5Mf1xl9utK6LT+0RPXhqwfHO+R/VVzXNPVu8Nfa6n6qsGX9BDfRUAAABwI4EpB5jgb0MAwJRwfRUh7Jekjm4AAAAAAAAANgjlgLWY5RlV23hDurjtbq3Ay/ljooM7sRXmsolCOQNMOVVFdP+j7uvVWRzgSJE3T0s26sjvZeiDwwzFojkmFyG0wYGNj/8q0e+9i+jkFWNszaS5N+Uoq4tpylnG6+exMNJC48M9MhSzxsXR1TnRJ96nxrESoRw25ej6qsac0grliGALL9tZX9VjyuFQAZGor1IXg6P6qpEXin191QN3LDhwI8NF2mgjg19f+1eIvuY/d19PUl814QXuuiZaNeeMtDBZ9VXm+MQY2VTUNb6xx34orfqqdYwvYkz8+jfj+qqe/doEud5BFVZGiIb3n+ur8mK4KcevL7GP/nFNHFN8GhoAAAC4ocCUAwxgbAAAbIWe63g3GbzuAgAAAAAAMAiEcsBa5HlGq2pLphyiRCjnEdHiTrDC7AIZajh/1L/8w0+6AFFWuNDIEFOOr4CRppysHcrhEEqxCMdEh3L4OK1UZRNRCIrk87BOuY+mKUfV1fDPeMxRKMcwq6wTLPjAPyP6P/5doicvi3WvQjiFx1+p+iquQIosNUs7lNNnyrHqlMIC4Xf+seWggGHK8eGW9CZb2ydy9VXL0xBcs+qr2Hoi66u+6W8Sfck3N+MYEspJTHBsI9ASBanEsYpCOTrg1GPK6ZqU2VZ9FT/POJSzTsVXFG5jU46or5q6NoyR633pQ/3LR3VTygjFz8VRphzDvOO3M2gFYj0AAAAAuDEgiANMENgGAExIXbs8DuqrOsDrLgAAAAAAAENAKAesxSuPz+n5lwbWN42hK5Rz0dRXsRVmF1Ql+U/IDKmveuUj7vZ1X9SYcur+UI6sqyKKAyEctqir2E5RHLhjsuRQzjK+Xaogit8XMuqrOBxhTHKb9VWrULNj1VfpaqWxnD0gojpetwwCsZ0jGlMTfspU7U9VhsfBj6nHlCPXpffHf8+fmsriZfQxtX7XB4/1/KELf/BzJAq0qGCCrK8iCsfKqjFrba/HlDNlOEQGiyJTTh6f60TpUFArlNNlytnCPhCF/ZgfUa+tJ4k41i1Tzjbrq8SxGGTKocZCJYI3vr6qiG+HmLGS9VW8LX5u9VzgQygHAAAAuGHAlAMMYGwAAIDLBa+7AAAAAAAADAKhHLAWv/b8/e2seEh91exwt6acw3vu6yH1VfebUM7rv4xoNdSUIw05za3/mQh9RKacRWPKaUw1/Luyy5TTTGK3TDkcprFMORzKUYYTDg9EIQsjfLLOxLnfFxm6KcMx8fVVypSTFe2J/tKo3+JxpgIE/JitU19lmYVGh3KEKWd16oJpRHZ9lTeWqHOMz6UhphwriCO/njL8IPfBPw4rd05miYCTdfyJmseoJxDTdW5vAp/3s8P+YFAKq75KmnK2FTpZx5TjQzTNsdf1VT7ANuQ41/2BqiyhCtfhOgAAAADcHKIgDiYCQYMO9AMAwEY0ptiuSmXgwPEBAAAAAACgE4RywNXCm3KMwMv5Ixfa4QDKLpChnPMBoZxXX3AWmae/oDG0DAjlZMo2Iat8+LYq41BOy5Sjgixdppxcm3JW8e8lvD5dX8W1PaYpp46XHQvvS1RDtTJMOSoo5IMD6n5Ecc1WX6VRXYbAh1w+LNB+fFqVS9KUU4b7DUGu4/S1hClHbadlymlCD4PqqxIVQvUWAi3alFPXzroS1VfxeBJKZG/K6bCt6GWnDnBMEsoR+7U8iYNJ2wzl+Mczc6ac3gtpXF8lAlDelMOhnNRzxVpdor5qyPMDk3EAAADADQamHGCBcwEAMCHyQ1jABqYcAPaHT/8G0eOXdj0KAAAA4MaCUA7YiAeny/6FxtBZX/W4qa86CAaYy6ZaER091YznUf/yqzNXaTM/chPXVdn/hj7X9VW5EfoQ9VUzacpR9VWrAaacQptymuCGacppfqYDIWzKkeEfbyVZw5TzM3+T6N1/t9mWFQRaCVPOvP17Dj/lhQjI1GGfTMtMIrBSV3GF2JD6KlKhnI3qq8Ry5w+DTcraB28/UqEcIhd0GRTKSQQdtlJfJfehFOM3QjlJU46qr+q6ELS1+qpmP+aH7SDYULQph59TRGG/tjHhxMfimbcSnb1G9PjF7uW9KacQJigV8PP1VQOOQ199FRGlP5UIUw4AAABwY0E4F1jUiSA/AABsAoI5HeB1F4C94cf+AtGv/tCuRwEAAADcWBDKAWvxhc8eb2fFXaGc88dEB3fdZPXqov37y6CuRH2VMUYN1yzJeqfB9VUcxCnir3kcHEzyphwZyrmIby1TDgcHdICjy5Tj66uW8c/YlCPDUj7cI0M5A8MKL/wy0Qu/0qyTTTm6vooDAJYpRwRprAqplWHKSQUIqjIEfPT+EFFnfVVnKGekKYfh50hXfZV1jg0O5aRMOVuwzEhjUbUK4ytEKMeHu9QxbY1RVSpZbMuUw/sxO2yCYBOYcri6iih97k0BH9/n3u5u7z/fcwdRF8bj8fVVzXNy3foqonETKaivAgAAAG4wmPwDFjA2AACmBPVVvcCUA8D+sDyNreUAAAAAuFQQygFr8Z/9219ARETLcuKJ0FR9VVU5M83itgu47NKUM6a+qi7dBHUhQzk9n7DJtClHhnIMU07RmHKIXMUXkQuw1HUIqlimHG+30KacjpoiH9jR9VUiQODHaJhyqoHnS7lqB4siE04pTDmLeDkeE0/0WyYc0zKTqq+q4mCUGcrhx0fV9ljHchNTDpGor+rYB11fRdSEcgaaS6xtbyPQslIhLj6n1zXl9I1va/VV50SUuXOxLxiURNzn4kSZckbUQY3ebHNO3HrG3Z497Fm+apttdH3VKFOOqK/y3zN9n0ZEbQUAAABwY6nxdwAwgCkHADAlUX0VXlds8LoLwN5QV/hQGwAAALBDEMoBazEv3KmzvVCOstAsm++5vmpXqe5q5Sbe58ft4JC5vDLlLE+Gm3K8MccK5ZQhhFLMQ+jn7EFYT7kcZspJ1VdZARqefI+sNGUw5RCFrzepr6rE2H19lZjgr1bBlONDOUZ9lWXzIIrPHz4OqQCBXJe5HH9yisStNuXIENOmoZyO+ipe9yb1VakLTVsJ5ci6syqML5/HVqjoVo1P11cNMeVMXl916s57Dquss355XJdPYlNO8tybAF7nYVPLd95Ty1c357sVeOPXEv3Y9a0vy7uDR1lG5nmJyTgAAADgBoP6KmABYwMAYAugvioNTDkA7BE1rp8BAAAAOwShHLAWs9y9IV2VE/8hNxehnLMHRK++4L7nieKDO87Ksqv6Kg6DLMaEcmYhlMNWiC66TDm+xkZYcIpFmMCPQjkXIbjRacqZibGRqJ2yTDnNz0pVJSWtHhzKsSbXh9ZXlcuwDR3O4THyceLKHF6urslV4jTHzYduRCBFhpT6wiZ1RZTLep2u+qqE3cWqrxp8wUItx6EcXddFJEw5W6ivqrYQaNGmHHlO6jojHXTyY9ShnCGmnKlDOefhOZBl6wWXOPxF5HSykSlngAVoXfhYHD3tbs97TDlcN5UX4b4ciNOBwqGmnMwy5Qx5fqC+CgAAALixIJwLLGDKAQBMivgQFl5XEuB1F4C9oa4IAToAAABgdyCUA9ZiMXOnzsXUppxi5kIdF4+Jfv7vEP3In3U/56qoxR1nhdlZfVUTsjm4PaK+Ko9NMr2mnCZkIsM5Vj2SVV+lQzmdphy2qiRCOdaEulVftS1TDgc2dH1V1ag2tSmHLT4ypJEXtq0mMuXU8XhbY+EqrCJeXu6TD+WoizWWhWeq+qrIlKMCK5uYcqL9M0IP2zLl1KWoQTLqwvQ+6nGNCeVMbso5I5ofNeMo1jtGdR3O5YuTYL/idRJtJ3jCx+KITTl99VW1qK/i546qr9IVdr0k6qvkpxGtC3ypcxUAAAAANwz8HQAaYGwAAExJVF8FTPC6C8D+UMOUAwAAAOwShHLAWnB91eSmHKLGQvOE6JXfJ3rwKffHojfl3HYGiZ2acgpnKxlTX2VV0aTIlRUnN+qrqtW4+irLlNMK5fAEe4cpR9triNw+yv1jw4dfTxUvO4TSqq9S4+JxcxCgNEI5skooCuUY1U+pAEFdxRVirWCE+ORUypRTW6YcGvZGKBXKsUw5VZ8pZ6C5xBrfNiwzUX3VKq5B0nVGg0I5PZaabQSLiFzozZtyeoJBKeoqPI+WT0LQjtfJy0wNr3Nxx53nffVVfL7LY63rq0aZcmqKgodmTVnqIihMOQAAAMCNBaYcYAJjAwBgC6QqlQHhdReAPaKucP0MAAAA2CEI5YC1eHTmAhAvPdqCsYZDOQ8+6QIA5w+JTl91vzt6pgnlGOaXy8DXV912Y+yjLt1E9xhTjjfkZOF7vg+vR1ZTFQchENCqr+ow5fCEOU+k62BHlylH11flsxCS8fVVG5hyolAOm3LUuHjin8fv66s4bNTYbfjCgByzDClZJh0J2458SEQdFw4VEFEID3DAqau+igaGcnR9FZtyrAouDuVYppxijfoq4+tJ66tkKKcM44vqq/R5lPhUh69A6jim2wrlrM6IZmzKydvnyBCk/allyhlrnhmBfD4d3OkP5USmnOY4luJxIxJmn03qq4h6q/6s0BgAAAAAbggw5gEDGBsAAJOC+qpe8LoLwB5RE56rAAAAwO5AKAesxfs+5kIyP/fBF6dfOVtoHnzSfX9yn+jkFff1rdc19VW7NOXMmuDQCFPOTE6w90w0c9gkE6Yc/hmvZ3Uu6qvmwbCRqq+yQkzeOKNDORzcMMIPlnWmbvbRh3IO4mXXra9qhXKUMUfXV3mjjrDFWDYPotiU0xVC4jHnRfO4GSaWzvoqY9spa04KvczBnfY+6MDMJvVVqQkOK2S1KXxeFgdu7KUVyhFhHD8WI4yx6/oqPu/zNeurqI6r2KQpJ09Up01BLZ4vB3dHmHIK8dzh+iq2V40w5fDj2mUDSn4qEZ+QBwAAAG4sMOUAExgbAAATw9eCQAK87gKwN9QVnqsAAADADkEoB6zFd3zDW4mI6I+/9ZnpV744doGc5Yn7/vRVotP77utbz7jJ6tUWDD1DYCtMsQgBgiHLzwzrRQpvysnD99qUszwV9VWLhClnSVSeh+WtsRERFVxfZQRI9KS6N+Wo+qqsCOEeb8oxamgG11etRFWWCuP4UI425ej6qiK2lshtm5aZRJCiquL6sFH1VWW4NeuX1qmvut3sQ/PY5vP2PpimnIGhnN76qilDOc0+LG7F9VX5TARRjO1aX2t7i8U2KriImlBOc95PUV9FlDDlbMEGM9qUU7WPtQwIEglTzpDQWaK+atBzA6YcAAAA4OYCUw4wgLEBADAl/JqC+qo0eN0FYH+oa1w/AwAAAHYIQjlgLe4cuiDHo/Mh5o2RLI6JXv698P1pY8rJcqLDe27yu1qGaqDLpFoFc0217F+eq4/GhHK0bcJbWihM/K/O41AOT+afPwzrKS/CZHmnKYdDOVy5lLC6EIV9btVXGTafTU05HNjwphwO5ahxcxCAxybNH9JaIh+vpRHK6TLlSGtRy5TTUV9l7fvYIEErlNPUV3HgqjBCOZuYcurEBMdWQjnN47C47R43s76q57hJUw5l3Z/4sM7JKVieEc1lKGeNi1F1HQJyRLEpxxuYtvCaJ18HDu7EryHm8mzKEaEc/7g1z8UxppxWfZU4dn1WMev8BAAAAMDNAKYcYGJ9EAIAANYF9VX91NENAOAqg/oqAAAAYJcglAPW4s6Bm3x9fLaNUM7tYMkhIjp9zYVyjp52k72+rmgHthyuryrmwwIOHFixrBcp8jxeLjdMOauz2E7hTTkylCOCLaYphwMcur5KTKQnTTki4ML1VRyOKRZuvLxsFKAYMklfh+qtuhbGHF6fqmji8XN4R9cZWZVbpiknFcopRT1V3j4mUX2VChZYxzJloknByy+a2ioO5aya/ZWmHF/dZYQZ8mINU44x1imrn/j8nB81phxRg9QK5fSZcvL+QMw29oFImXKy9dZf1/2mnKnHTRTOiawgOrwbv4aYCLNNK5TThIpGhYhEyEfeJ3ocM/uaAUw5AAAAwA0Gphxg4O2kOCcAAFOC+qokMOUAsB/UCNABAAAAuwahHLAWxwcuEPFoK6Gc4/j7k/vu31FTlcUBlF1UWHEoJ5/FwZQUdVPtJE05ffj6KnHbCuVoU45VX9VnyuFQDts5hphyOOCiTDlZEZtrsiLcd4gpp6qI3vODLoDlgwe1+7plylEBAB/S4tBOsx950Yyjin9PFM6dWnxCoMuUwwGg3voq9Qkquaw2/ejfp+Bljp5yt7NDt1/8mEamHA7lTGTK2Xp9VRNmyefuuPhKtXl3nVEylJN1j29r9VXnIpRj2JSGUFch2Eak7Foj6qDG4sN5ifqqk/tqnOSOs7RG+YAgh3J4vANDePzYEal9HGHKwVUFAAAA4GYBUw4wweQwAGBC6roxu6K+Kg3CkADsBdu4rgsAAACAUSCUA9ZiVuSUZUT3n2whGDO/5W45bMH1VbdeF/+cwxqXRVU1AY1ZEyIYYcoZVV9VxLf/5n9C9G/9dfe1DySdivqiRai6aYVyOkw5dUlEWTDzaOsFj19SGvVVbMrx1TWzxsrCoRzxxjwVfHnpQ0Q//z8R/f7PxoGf8jzsgw7lZOo4+fCOqK+ybB5E7vjJfeb9sKjK8JhZJpbB9VVWddIIU86hCOXkMxHKmrcNMJvUV6WCDv7xnDDQsjxz53TeGIh84EoE0SrjTaNpG8oSoSnBtt6Ark5FKKdnDEm0KUfWV+mA0oTIujcdynn1BaIfeBvRJ94Xj3Or9VWpfTSeK5iMAwAAAG4wCOcCA9O6CAAA6yI+hAVsYMoBYD/AcxUAAADYOQjlgLWpa6If+dUXpl8xm3Luvono4C7R6avO1sChHGmLuUxkbdLQKqCqdGEIHjPRgFCOqoD5oj9F9DV/2X0dmXJEfRWbcmSlV3kRKo4sUw4HhnhMVnhFT5BXokKKwxJsyimEKSef9RtOJBwaknYf/X3KlJNlLsDQqq/SwQGxXm/KkSabDlOOtBbpfeBPTvE25Xqt9Q8xB+ntEwlTzoHbd96HfIwpZ2BIwn9tPHaT1ledhZBRXYbHOp93H0vrnBpjyqmmDuWch8Bclq8XXKqrOIhjBfm2YsoRr2s6lPP4RbfNx58V4xSfFNTPrXwNU06qvko+xlYdm14Gn/QBAAAAbhYI5wILnAsAgK2Q4fUlCUw5AOwFCC4DAAAAOwehHHD1WNx2t/c+j+joaRfIOb1PdEvVV528QvSP/gzRix+4nHHJMEgxH1FflY8z5ej6KknRVGetzlwIpVi4CeuZmMznifHyIgRVUqaczArliIl0HcCILDrLsEwu6qvypr5qTAhlJUI5kdHmwqiv0rVb1IRylvE2ssIZWExTzll7PMlQjjTlWKGPofVV64ZymnUd3nO38yNlyhEBKFlFpBkaJKsTnzrexps3DrOwxUc+x3RlkxUQkl+zGanrEx/bqq9anrnHhcexbn2VPqcZH1iZeNxynVnhQpCr03btXfTcqMOY+OdcDVdoU87A89s/dhQfu2QYR9zX+hoAAAAANwD83w8sMDkMAJgQ+SEs/L9jA/sGAHsCnqsAAADArkEoB1w92JRz7/NdEMfXVzWhHJ6sfvFfE73wXqIXfuVyxiUDA0OtIxxYKWQop2eiWdprLGaHwZTDx0Ku/+Cuu5X1VSlTjqxdsgILOgQg99mHYMpQ6UXUBIdyYdUZEEJZnoV1ylqy0grliIojRgZOZB2PFRyQ2+urr+KLC5FRqGwvI+ut5Hor41imTDQpeBkfSjt04zFNOSKgohlaX9VnypkyGMKmnKwwQjnqWA4N5XSacpqxT2n7IWr2o3kO5oZNaQh1nTbl8Pm3jckFbcohCrYcNm1FrwlcNyX2s1q5770xaoQpJ1VfZdXE9a0HAAAAADcHhC6ABSaHAQCTk/VfxwP4fxmAq45pIgcAAADAZTLrXwSAS8aHct7kalMefNIFM3x9VTNZ/don3O2Tly9nXK1QzlBTTtEYJDIiqtNhG4Z/b9lOiNz+r87ccmymkBP4h3ddkGl5Fv7QNk05VX99VcuUo6qleJksD+tiU04rhJKlwxCrdeqrxPGxtpcX7eCA395AU04lAj5826qvqkTFuPoEVbT+ESElvX4ioq/6S0Rv+Qb3+Mrzr1i0AzPWOVbMJwrlTPjmbXUe6quqKoyvMEI5lLCi1MLc0hvK2cI+1LULv82kKWeNi1G6vqq4pPoqbcohIjp/6EJg/nmij7dRDSctP3kz3kHhp1R9FZF4YiXuivoqAAAA4OYCYx6wgCkHALANUF+VBGFIAPaDGn8jAQAAALsGphxw9Yjqq54huv+8+16Hch5wKOelyxmXN0rMhltHqlVj/cjCuPtCOUNMOUtRX0UUT+bzxPrF4/Azy5RTV6q+qg5j9st01VcJM01ehIBQMW/MNTokM0ubM7wp56Id/OHwjw7nRCEAY3s6pGGFciyTjUSbZ0wLSld9lVEFNjY8wMvfeQPRH/3WZhyy5kjWVwnriWao3Sm6kCInO7ZgmWFTDpuVOk05iTCTf4wy6r1Qti3bD5F4fneEz7qoK/dYMvx84nUSTW/4IYorz1qmnER9lX5ulSs13jGmHErXVw26s/U1AAAAAK49qcpVcLPB5DAAYEpQXzUATPQDsBdYH3oEAAAAwKWCUA5Ym7c9d7ydFXtTzuc5WwOHMo64vopNOR93tyeXbcppAijlAFNOJWw0g0M5zcR81mPKWYlQTmTKueduZShnmaivyg0DTBTKURPk8neyViorQn1Vy5TT/LFfzNMT7t6Us4xrpiJTDq9PhKMYGZKSdhtZN8Vhn9nRcFOONs8kTTmJ+qopTTnSGhLtu6yvEtYTjaz4Gro9y0gzqSmnqX3iwBY/1lEoR51HrTGwKYftLV2hHA6ebSGUM2dTzpr1VdRRX3Upppy8HcoprfqqWhxr8dzSITmiYcdZ11dVxkWCLIu/l2OR6wEAAADADQKmHGCByWEAwJSw2RX1VUkQhgRgT9jCdV0AAAAAjAKhHLA2H3npyXZW/OavI/oj30r0eV9LdPR0+HmrvqoJ5Tx5ZTvj0Oj6KqrFBHKCugyTzb6OpufNPAcq8pQppwmVlBfBTiEnxH0FTRPKWdwJoZfW2CxTjmF3YaLAzNLdp67EMSFn++A6IqLYlJOapE+ZclYXcfhHjqlVX8Xbq8PPpNmG73dw29UmybHx8dDIKiwid5z0Yx59ckp9gioy8fQYX1JoW48cD1Ecduo15QwJ5dRiHVZ91RZMOVkTGPLjn4sgivGm0TTl7Ki+aqlNOT1jSNGqrxJfe/PMFt44y3PGv3YoU04rBJU1zy1h11rXlONDVUbwqPfCJ0I5AAAAwI0FphxggU+BAwC2Auqr0iAMCcBeoK+bAwAAAODSQSgHbMy/fH7iUMzxs0Tf8g+IDu8GOw5RCOXwZPXDT7nbS6uv0qEcigMk5n1KYco5dLcb11cduFBJeRGCPlkWvj7k+qomNHV4rwm76CqqMt7WWFNOtYpDK1y9k89doMgbTmR9VeIP/+WJu5V1Vfp7H8ppbqUNJs/bJh22b3CIhi0si9sibCDGYwWsqgGmHK7zkctZb3R4XZWyjvRhhnKUJUiHTUxTztBQjliHFX6Z1DJzHkw5VRnbqHIdREkEMKJQTnb5oRxfXyWe32uFcih+XC/LlCNf11r1VU14TZ+zWRYf6zJlyhkYOuPHjr83l+sz5eCiAgAAAHCzwN8BwMALG3BOAAAmoK57P1d344EpB4D9AM9VAAAAYOcglAM25rMPjWqkqbj1TPtrnvzmyeRLq68StUlshegLOUgbzawJE/XZH3zAI1VfddiYcpaxnYIn8Xli/aKZWOc6q5V6nKTFJyM7lNMK8qhqKRla8aaceTCfkFgvVxRZ8Nh0fdXqVIyLa6xEiICR606ZU/h+B7eD3cSql5LokIsV+qgr8ldpfLCAPykkrUMb1lelQjnFvG2TsQJdXaYia3vShEIkQk8Tvnlbnbnap7xoQjmyvkqFNAaZchI1R4wVjNoUM5SzxvpbphwrlDPhuP12ReUZV3w+EAAAIABJREFUv3acPXC3Prwmt8v6bvXcytc05fiQjwph1c123ApTdxZfwpQDAAAA3ChgygEmmHACAGyBvmsNNxqYcgDYC2DKAQAAAHYOQjlgbf7aN34RERF9/jO3trcRrq/KcqLDp9zXs0W8zMn9aSfZU0QWjyYUUfaZclZh2cGmnFn3crMDUV9l1N3o+ioO5SxVKKeq4voqX7kkTTldoZxl+H1ehEn5fBZCFkThj31pdNEsm3qt8jy2D/E+ELUDFTKYYoaAijggwetd3LHDBl31VTIo1QrldNRXWfVYrSoggz/8ENG/+mF7DEQqlLNoW2ys6rO86A+RyTFll1FfxaYcrq9qxlfI+irjTePG9VU9+3D+iOgffCPRZ39nwD6oUE5unCND0KEc+TrXsgZNSCWeL4e6vootVaqGzVuo+Lm1CqYsonD+DRpvHR67wffhu+IT8gAAAAAg/B0AAjUmhwEAUyI+LILXFRvYNwDYL/ChNgAAAGBnIJQD1uaPvdWZa+ptvjHl+qqjp8NErzRIzA6JqHbBnG0T1VexKadncr+qRH2VqJrqgpfPE6ac+ZEdyuH1z4/cGH19VTPRvjqN11NX4ZhG1gtpdzFCObOj8LWskuLxsinHBx+a86PoMLV4U85FHHTifeDtyTHpuhwd2mmZcpqfL45DLU9kyukI5UQ1X3o5q77KCOWMMeX8xj8metd/HS8ThXJkdZesrxLWE00+6w+RybFnuR2Embr6aXYYTEfysW2FcoaYcoaGcnr24dUXiD796wNDOc25NJemnHVeE+s42GKacrbwWluL58v8lrv1oRyj5q3mEI0IH+n6Kh7vYDNTZoRyxDZTn0oca50CAAAAwPUBphxggslhAMCE+A9hocMqDcKQAOwFeI4CAAAAOwehHLA2hzN3+pwttzgZeqsx5dx6XfjZTExWP/el7vYyKqyiUE4Teqh6Qg6yvoon2ftMOZG9xmB24IIA5UVcX8UBnfmR+/qix5Qjx2bVPPEykmoVwgeyvkpWeuXzpipJBSC66qu8KWeZDuWUur5KmWN04CLLY2uJrK/igJJlson2lwMLXE9lhD666quigJNlfEm8ITp/1DYNmaacTO2jMBdp8tkwU443G+V26GFKK9WyCeWw6aiU9VUq2BGNxTqGzYWyIaGcvn3gUMqQ48XnrjRhrXOM6iqugZOmHF3lNSXynMkyV2HlQzkcXruM+irDlNMXYCRMxgEAAAA3FxjzgAFMOQCAbYD6qjQw5QCwH6C+CgAAANg5COWAtVk0oZzz1Raro7wp55nwMxnKef1XuNsnL623/tPXiH7yPyV6MiDUI0M5HEDpm7SvyhAemQ0M5XgrS8KUMzvsNuXMDt34OJRzkDDlVKUywBihnEqFAKqVs2kQNfVVwiTDk/LFvAl0qDBFPk+HCqQpRwadLh7F25a32syhTTRZEQckOPCxuJ0w5Rhjk+vi7XTVV+nwhFlfpaqALM4fEVHt1u3HIAIKvO++oksHklKhnKHmEl7HZZhyDkKISz62ljnFCm7I0FKfpWboPnAoZYhZiM8lacJat76KsvDYWqacbdT0abvSwV2x/+ft7dYUQjTyNaNQ5qrB452qvgqmHAAAAOBGAVMOMDGMpQAAsDaivgokwP/BAOwH+BsJAAAA2DUI5YC1OZi5ideL1Rb/mDu46yZrpSlHTla/4cvd7ZBQjcVnf4fod/8Z0afe37+sNErwxHnfpL200QwN5fDyeWK52YEzjJTLOKDEAZ3Zofv6vMeUc/KysFtkZIZytOmiXDkTD5HbvqyK8mGCeTCfEMWmHCv4QjTMlFOpUEuqviqqM2qCJXUtTDl3RC1PjylHVvvwdqw3L/4x5Ys1/EZHmnJG1FdxKCIK5YjzgUNhmQ7llO1lmbzoD5HJMeWFHXoYYj8ZQrly65oduXNd1qHlM2FbEZ/k8D8zxjWovsowGFmcP2yWG3C8OOzGtW5Z4hzpg2uhvClHhnLUsZgSbVc6uBP233qeWKacVn3ViPG26qusTzdnibAVQjkAAADAzQWmHGAAYwMAYEpkfRVeVmyG2KABALvHqosHAAAAwKWCUA5Ym2DK2eJkaJ4THb+e6Pbrw88KYdF4Q2PKOXllvfWz5YKtMl1E9VVsyumZ3K9WxiR7z6dspL3GYnZk11fx+udNKIcDLYeGKedjv0z00fcQfeW3hG1Z1T56/6pVCB9US3FMimDKyOcqJFOHZcaacs7F49JpyikMM4+yb/D9FseheqtrX6N1SVNObSyTqK+Sy1o1TKk3Qj6UU9qhHB4PG2X8Y6fGKxlaX8VjSplyUsGqsZQX7raYhWozHl8xb5tT6jo85lawyVcg1ekLQfocSTGmvqplyukJBqXw9VVcdyfrq9awyAzerjblyFCOUV/FFyVlvVm1jOurxphyuuqr+l4rcfEPAAAAuLnAlAMsUM0AANgGqK/qAO/LAdgLUPEJAAAA7JxZ/yIA2Bw0oZytmnKIiL7tx4nufE78s+LAhUye+zIiytavr+IwiDSypIhCOTzp3GPKqSoxyT5VfdWBXV/F658duVDD6avue23KqSqin/lviO5+HtHX/7UwJv6bvFqRmwyv21aUSplypLVGm3JaIZlZ2rLiTTkXzqDCRKac5lhXliln1g696EocGcohcoGDaFLfGFulzDNZ1g4a1HXIDuhgQdVnykmFch6KZUU9E5Osr+oy5QwM5cjjZ5pyJnq+yzAIm5V4fNFjJ/SquWFg8aaVLA5FZUagY6jtZ1R9VfO84udFlvev36KumrCLZcrZYihHB7kO7jqLFlEI5UTnvGHKqco4IOjHO6a+SlW/Wct1/QymHAAAAOCGgQkFYAFTDgBgSlBf1QtCsgDsBzDlAAAAADsHphywNmzKeXA6YOJ6E9701UR3Pzf+2ezATeTefj3R0dPr11etG8rhCeht1lclTTmHROU50UqbcpqADptylifu+wNlyvmDnyP6zG8R/am/TbS4FbYl66t4rJYpx4dyLkRopQimDA7oVCrEUczTE+dRfdVF+PlFhylHm2N4LFGllggzcMUOm35WZ/F4rMCKD6hIU47eh7qjvsqoxxoUynkUljVNObPwMxme0VVEknzmxtVnuvFmo1QoZ6L6KrlfHKpis1QUsBFvGq1Qjjz+/hilTDlGWMpijCmHw26zQ3fbZYTqRNVXmaaciY59tFkV5Dq4E/bfm3LUeZBRd33VWFMOJUw5mTJQmfcV4wIAAADAzQHGPGDhA/27HQYA4JogP/CD/2sS4P9jAPYD41o1AAAAAC4VhHLA2vA06fe944OXv/HZAdHxc27y9/i5DUw5XF81JJRjWGH6Ju2rMgRDeNI+NcHM+GqiDlMOkQusJE054ufalPP4RXf75q8Py2RZHMrhdZmmnFvh61qEQDggVMybQIc25czTk/Sp+qoolMOhG6u+ytheVoSJfhn44OPXCuV01Ff50IcRuIjqq3TlklGPVZUhwJR6I8SmnErWV4nzJjLlZO3tWZaloedsFETaoilHBog4VFWKGiQdRKmrsF/alKPDG6kxjjXlDKqvUqGcdeqr+MKVDOVIU44ZRpoIbZ46uEN0xvVVzb616qvyxm7E57QK5fjHadP6qt47J74GAAAAwPUHfwcAC5hyAADbAq8rJjDlALAfoOITAAAA2DkI5YC1uX24w/az4sBZcoiIjp8lOnllvfWsZcoRVpjegINlyukL5QgLigVP/p8/jMM3kSlHGHQOn3K3bMrRIQLelrSI8Lq0VaVaufUTuQBFJQIcPkw0D3VERCKUU6Qn6YfUV5VD66uE+UOGODgMw/utQzlWGEAHYqzARS1NLeoTVFbop04ZX8T6ek05ouIsqq8ylvX3GRjK4YsoeRG/UdMmok3xQRQO5ayax0id//JNI/+uFcrJ7fu0tjnwDehaoRxhwhr7BtePN2vX3fE6ibbzxlm+rhERLW6H5x1bq/rqq8pV/JozxpRDiVBOtK+Zve/4hDwAAABwc8HfAcDCvw/DOQEAmAJ+/4sKqzT4/xiAvaBGcBkAAADYNQjlgLU5nCVMLpfB7IDouAnl3HrdBvVVY0w5wtDiJ537TDmrtvmit75KV/Eo5iJMIyfCvSnnUJlymvoqNuXwPksTxxhTzkzWV/ExyUNQqZjFdVJMPkuHJbwpZ9lhylH1VbouR4eAuNqJ96NauuX4+K3ObZNNtL+qDkoaeRhpahlaX+XHbrwRWp7EIZuu+qp81g5UyfFKRpty8nh8U5tyfHgqa84N8RgRtYNL8rilQjk00JTTW1/VmGL66umI3Lmbz8U5ko0PLpmmHFlf1bNfm6DtSovj5hyshSlHVa5lKpQjX+fkuoaMl01TZqBqxIVP6HcBAACAGwY+mQ8sMOEEAJiYLKPkB0WAernFMQLgyjL1dV0AAAAAjGaHqhOw7+T5Dj8p8nXfSXTnje7r4+eIPvbe9dbDk87LkaEcDsN0TdrXdRMkUOaLvlBOb32VDOWIYA1P4rdCOU19VZ8ph988VythyrHqq47C17Ww1vigSGPKadVXzdrmHSYy5SzDes4fh/30oRwjeGJur4iDHVVj8+D9Xp7G67AsPq36KquaqPnklFwuCn9kbhkZGuoy5XB1EP9eBjaYqL7KMuVsEsoRBhsdxiBK247GIh8nNitVwrjSCmkkDEOmKSdxIUjblFKMMeUsz8JzgsiuOOtDGpn4cbJMOVNZiiSVeByIiBa3iKh2zw8O8NU9phxdXzXGlJOsrxpwMQ+fkAcAAABuLtHfAbsbBrhiwJQDAJgSvJYMAO/LAdgP8DcSAAAAsGsQygH7yR/7jvD18bNEp6821Tcj7T2jTDlGAKVr0l4HJAabcoru5aThRoZveBJ/fhQbdA7uuNvIlJPFy7Tqq5rgSmSSaawtc2nKEZaNgsMEjTWkUkGRYj7AlCNCOYvj8LjMjwxTjnisZeCnEkEaGdIom+AAH7/VeWwdsgIE2iJiVRN11lfV7jEqz+NAiDe+GG+EOBBC1G/K4X0cZMoZGJTgMeVFHDqr1fHdFH/u5MGiVC4pXV9V2aGb6Pj31VcZBiOLsfVVMxWgGf2pEw5CyVCOfH6OMM+MRZ/j82N3e/EkPC/lOcMhmlyEj8pl+/VErrt7AOq5KvbR18Y1wTbrvv5LfNIHAAAAuFnAlAMMxgS8AQCgF1lfhdcVkxr/HwOwF6C+CgAAANg5qK8C+8/xc0RUE53cH39fnnQeVV9VhKqmrkl7H5BonmZ+4r7HMORrcIaYcmR9VcKUUxy4f9KUMzuMO7FboRzDlMP76kM5S2HKEceEq3wsc41po6mFKUfUVy2OQ33V/JYwzYhwFJPnRn2VsG9UZRPamof6rdWZMq4MMOXkhgVF1lfp0EhdhseoEsfDqmFikqEc8XhJm1JkyhFBF82Vra8SRqPyIpxHVijHh43WDeUMra8aE8o5D+cUj2GsTUge87xwz1/9/JTLTYl+nVo0oZzlE6LVhbFdcVFSHs/cCuWsU1814uJAjVAOAAAAcGOBMQ90gXMCADApqK9Kg/+PAdgLYBMEAAAAdg5COWASPnH/ZHcbv/U6d/vkpfH3HWXKEfVV3jrSZcpRBoqhphxtCtFEoRwRvuEgjTTl5HM32T4/jE050uzB2/IT7Ktg3ak7QjnVKjbleMPHLNQREYlQzsyeOF+dk38TvzoPtpRiQbRszquFCOXIx8GP3wgBZUUcDqiW7nHzppyz+I2IFdTQ1T5ZZiwn6qv8rTCy8GMhx5cZ4RLmXNdXVe1zwZ8jifoq05QzMJTDY8oLNdkxsPppKDqIQuQeE3+sOVDF+0Z2mIlDHUQiyJJ4g6nDSyk4lNNVT8esTjc35UTHYhZXV/HPiaY79tG2yzgAuLjlbi9OQmixVRdm1FcVE9dXWUaq9p3jcQEAAADgBoFP5gMDfAocADAl/H4VpIEpB4D9IHW9DQAAAACXBkI5YBL+9ace7G7jx8+625OXx993LVPOLAQtuibtdXikGFpfJawsFqlQzt3Pc9agYhF+7u05R21TTrzR8Ed5tQohA1lVxAab2aFbXtZX5YUKAhXCDMMhj5k9Sc/joiyYcvJ5HHSI6qtU2Im3X6nQSFSJU7r757Ow76szZQIaYMqxAhemqYWPZRkeg3VMOVXZHcrxphyxPTkO6z5VT9Dkskw58tzhx3J13lNfZdQ4maaiPlPO0PqqAaESXYNm2ZT68G+Im/qq2SL+fW7s91To2r/FbXd78SSEFlv1Vc1zi38ua8eIxOM0JJTTnN+++k3uow676fvi4h8AAABwY4EpB5jgU+AAgClBfVU/+P8YgP0AwWUAAABg1yCUAybh5ScXu9v48XPu9lJNOWPqq8aacoru5VL1VV/zl4n++vvdxQIfxmF7zlhTTnO/2gitsMWmVV9VhDFZ5pp8bk/S87gO7rqgT7ly65D7Nr/lfs7j420y+czYXhGHGcqlWycHKFbnKtxhhXJUyCUZyuFQCP9MBEl8KEeYg7pCFlZ9VTKUM0vUV3WZcnqCEimz0dDqp6HwxRppWVqdhcc9y8iFxXgMddqUkwpFtbY5IFhUVUQXHMoZYMpZnsbPSRmSGkpkypl3mHK28MZZm3LmjSln+YSoPI/H574hd1GyiM8JWV/lTTlDQkS8Ph2oGrKvMOUAAAAANxeEc4EBTDkAgG2xlffjNdH/9VeIPvbe6dd9WeDDMgDsBzDlAAAAADsHoRwwCbN8hzrXW40p58kr7g/LX/h+ovsfHXbfUaYcEUoZVF8lapSIwsR9n/o26wvliAl7acopZkSH95qv5/HvpSmnPG+bcjJpyimFKceor2IrTqu+ik05zfGpVACC6230H/88rkMO5VyE4A8zl/VVTYhAHsdMmnJEwCGqr2qMIIWsr5ImoCGmHMuC0lVfVbbrqyppymlvclh9lThHMhFc0XVb1n366qtkWKY2Qg9ThR98gCgT9VXn8dh1NVdumHDMUE7KlDOgguvicfi6t+qrGXMUyrEqznrw51lzLLQpp2+/NqGq4mPO9VXnj91zkSg+XjX111eNMuXU7eeqXw/fJj6VaJ2fAAAAALgZRHOAmFwADEw5AIAJ8R/C2tI1z2pF9Nv/lOhjv7yd9V8KMOUAsBfw8xPXzwAAAICdgVAOmIRbi0TV0qVs/Bkiylx91YNPEP3C3yX64P8z7L5sylmOMeWIqqZtmHJmh0THryd66s327+dHYtkDexlfX9X8vmXKMSb9e0050hQ0cxP20pTzxq8kesNXEt15Y1Pho801CVOLNOVQHWwp0hayOBahnFVclUPkwhq8XlnhxMe64vqquRjHqt+Uox/DXlOOrlyqE/VVU5lyChpvyukL5STqq3TIalPksZWmnKgGKY+PZZ8px/+sx5TTFZqRx7+rno7RdXBmcKsPDkLlTSBNm3Ka82sqS1G0aWXK4fqq01fDz6LtSrNN7Y61rq/Kc+N+qe3XIeRDNPLY4eIfAAAAcHPBJ/OBAUw5AIDJ2WJ91ShT7BUFphwA9oPr8HoDAAAA7Dmz/kUA6OditcOUdV64YM6Tl4geftr97Pxx930YacqR4QoLHUoh6p601zVLPpTT8wmbYkb0Nz5o207keojiiqdoHc0yM2HKWTZGGh0iIGqHcixTDu9rPg/1VfKYvOmrib6z+XRPVsR1TbyM/J6Rphwi91gUC1VfdaRCOerYRPVVwvSSifBLtVSWo2oNU07eDu/UFVHW7Bs/ttI65ANcMpTDL73GG6GWKadOh3IyHcrhY20Ev4aGcnhMuaphmry+SlY2CVMOh0L4d3LfMivMVNNwU86AT4XIUM4gU85Z/Jy0glt91CqU0wrNdYS4NkU/n7i+6vS+GJ8MQRkhmmoZ11fxmIeYclL1VUNCNjVCOQAAAMCNBX8HABN8ChwAsEdcC3MF/j8GYD+ATRAAAADYNTDlgI345q94IxERfe//+du7HcitZ4mevEz04JPu+4uhoZzGlFOtQlVLiiiUM8CUo60lxUBTDpEL5qTCOzJQUyzsZXR91VzUV+m6HR7TGFNOMW9COaqii8mLdnjDh3ISphyu3lqeNMEEEXSYHcb1VdqUE4WARC2Srq8q5sJSU6paHuMiiH4Ms9x48yLqq/xjKy6seFOOCCmNMuV0hHK0Kacq0+dXylSkSZlytlZfJcJTpimHx9tlykmYilrbVEYhi3VCOdJeZQW3+vDjzYi+/r8g+pP/Zfz7rdZXlXZ91UkilMPnOwe/OKynA4LWa4BFV32VrIUzLxqgvgoAAAC4uWBCARiMCXgDAEAfsr5qG68r1+E1C6YcAPYDmHIAAACAnYNQDtiI7/gTb931EBzHz7lQDptyBodyzsLXFz0VVr5uR9pW1qiv2rSLepApZxHfzrUpx6jH4QBIXQpTjmGS4fquahnCB9rMIi0Zg005TSjn4qSprxL7UMzj0E3LlFPEJhqixjoiJvrLZRNiEY9dZMoxHkttyslT9VW8/1l8PxnK8cejTB8LIru+Sp8zvP/5rF1fZVVXyfsMra/KZ7YpZ2zgpHc7sr7qIn5sZQgqFWaS9VVZ1v69tc2qI8DhTUXZsPqq5ZSmnIzobd9E9OV/Lv79NkM5+pyZH7tbacqR4RptyimbYKN+Tg415XCoqrWPY0w5W1KJAwAAAODqgklAYIH6KgDA5PD1mG2Gcvb5QyYw5QCwF1wLMxcAAACw3yCUAzbibc/d7l/oMjh+HdHJy2vUV52Hr3tDOU2YIctFJdEIU86zX0z09j/rap42YZApR4dybjkDDVHalEN1mHxnq09kyuH6qsYUVC7D8qYpR1cqJUwtHBY64Pqqx01FlrD95DNVX2WYcloVToUy5azcen04pYwvGFhWj0qsi8gOXESmFlVfJQM4Vn2VdcHiTNRXVWW8foZNTWwYkaacVO3ZkHNWjikrEqGcid68ecuSrK86jYNmuXxcaxHKURd9dH1V6mLZGFPO4b0R9VXi+STHPBQd/tL483iiQJSkquJzZrZw51dkylFGKRmiWTWGMV1fJV8DiIg+9E6in/3bxgBEdRdR/NhG532HKWedYw4AAACAPQeTgMCijm4AAGAzxAdotrL6a2CuwP/BAOwH18HMBQAAAOw5COWAjXjmOBEKuWy8Kaejvuqzv0P0/W8leviZ8LPVGflPvQwJ5eRNrRSHKrpMGjrQsTgm+o9/jOipz+/dnU6KuajE6qmvYoPH4laPKScPwRV5fxlUkb8r5q7uy1da6VDOrG3K4XXqyXNfX9WEcpYnrr6Lg0HF3K2vFKGcrrosHxQSlThV6UJF+Szcty6VccUIPPiwhKhHaoV3jFCIZcqRoZzMML4wlimnr76KqLEcVe1jo+8zOJSj66vU8d0Uf2ylKedc1Vdl8bG0jtuY+iptU7Lg43/09HqhHLPirI+eC31ddWebYtmVFreITl8Vyxj1VdqUo61d2pTze/+c6P3/yNg+11dxoM04v1LHJQqQIZQDAAAA3ChgygEWMOUAAKbk0uqr9vn9LEKyAOwH+BsJAAAA2DUI5YDrwa1nXd3Kax9331umnM/8tlvm1Y+Gn63O3eQ70fBQDlGwQnTWVyUCK1PAIYDe+qrm9/NbrhaKKG3KqeswZg7tWPVO+SzUSfn6Km2uydsBiFSwYKVNOSc9ppyyvb28EPVWwjoiQxx8P2/KqdT+WaEcZQJKmXK8zlhpjatShJGEmcefE8YbofNHYT19oZxM2YBkxZNmaCgnso9s0ZTjj620rpzHxpXoeKdMObK+qieUI41CKTiUc+uZEaEcVV81Nrg02JSzhTfOVdmun1vcJjp5JV6G8fVVbDfi+ir9nFTHoarCa5CkVV9Vx7ediAAZLv4BAAAANwxMAgKLMX9LAgDAGLYZytnj1yyEZAHYD3wmZ59DgAAAAMB+g1AO2JgvfPaYiIhefnzes+QWOX7W3f7hh9ztxaP2Micvu1sZ2FmdEd16nft62RfKKWNDCdG4+qop4RBAcWD/3gdamt/Pj5yBpq4HmnKaUE9UX8UBnELUVwnbiSQKyXDII1VfpU05T9z2/T5aoRy1PWnlkAYWWftTrZyBR5pyorCB8aZEhyUsI4f/5BTZwQIeR7USASWur0qYcvhYJEM5RbjNRICnKvttK32BEbnP2kgjbz/7O3HF0Vii84nPjWX82PIY9DkUjUuYimSYyWJUfdVT3SYsohDsksYqafcZyuBQziWZcubalCOPlzblcH2VEczTJqpqaRxTtb5oWyLsZl2klCE3XFQAAAAAbhaYBAQWMOUAACZly/VVfjP7/H4WIVkA9oLrEAIEAAAA9hyEcsDGPP+yC7P8wLs+vLtBcCiHq1QsU86TJpQjAzur8xDKGWTKYWNKU2HVWV8lQgdT4005qfoqbco5cpPd5TJhyslCqIMoBGJkgIP3lU055YUw5aiXEiskkyfqqyxTTjGL9yGfuQl9tvm0rByztpkny2M7T9nUV+U5EWVxSEbvK6MtP5YpJ6qv4lAIh3IaCwmPz58TXaGchy4QwvcfZcoxAhb6Pr31VXKfjYsrvA//+FuIfuXvda9ryHbk4yTHyb+rSmqFVlphoUQoKrXNTlPOQ6L5sXuO9AaYjOf4OlVKfryJC318Xk1VHSaxQm6L4zhwVavzIMvCawvbb/rqq3js+nWWQ1Utk9YYUw5COQAAAMDNA5OAwAKmHADAhFxWfdU+BwkRkgVgT0BwGQAAANg1COWAjfmi198mIqIvfsPt3Q3i1rPh63xOdGGEcriOpWXKecZ9Paa+iii2t1hs05QzH1hfxeGa+S13uzyxTTnEoRw25XTVV81DfVWV2Ec2V9S1YYdJmXLuudvy3K6v4vHIcJTcHtVxJVUuAisV328efifDHta4iMT+iXokfSGmq76KAzVZkTDl6HXVjSnnXvi+K5QT7WNpByz0faoV0T/9S0S/+L/ay9Uy6NBRX3XxuP8504V8fsjnVWHp4BPyAAAgAElEQVTUV3Wacqz6qlQoZ8BF+vNHRAd3mnOkz5SzCvugxzyGXlNOovptCqwg1+I43vcoDNSYbRbN6/1ZY9TJ1WsRP8fkdojca1C0fV1fJfax79OIPsu0hp0IAAAAAPsNJgGBxXWY4AYA3Byum7niuuwHANcR/3qz22EAAAAANxmEcsDGfNvXvpmIiL7vHR/c3SCORSjndV/UY8ppflfXLgDiQznGfSStUM68O5RTiXDI1PSacubx72Uop7wwTDl5sNDI+8tJdR/KmYn6KvGzaH0iRMB/9BcJO8zq1N1/fhSPn4NBxTzct1rZphxZSeUDDpmqdhL3Y4vHUFNOJk05armaRH2Vqk+qyhA6qVVgiKh9wWJ54tZ/9FRYT1TP1GCFcoaacsol0ad+nejFD9jLRQERMT5vIirDejYxt8hjkTTlFInjlgrlTFRfdXAnBM+IiH76u4je+b3tZbX5iMg+R/rYZX1VVbVfo/j1gsg9D+X+8CcFD5pQzulr7taslDOeXxcqlKPrq6xzKsvIvmog6qtwVQEAAAC4YcCUAwyGhPABAGAw/H419Z5009Vfg1AOQrIA7Af+byR8qA0AAADYFQjlgI25d5SwtVwmx8+Fr5/7Uhf0KFVg5qQJ5XBgp7xwt76+Sk8WK1qhnKInlLMKy00Nm25666tUKOf0Vft+bPeIQjmZqp8RAZxioeqrLHMNNTYaZTnRk+7LMzc+OSbeBo9VWl5qI0QgtyftNjIcVK1CuEebcrLcfix1WMI0cnTVVzWBkVzVMPnxqwsW5021Gpty+D5D66usgIW+T1U6w03y3BVBhy5TTrUaHz6RVOLYyiBRVAXFZqKhppy+UE4z3s76KjbliHq6z/w20Yu/a+yDEUrLizXe4Pb01PvHeAv1VXWivsp/fcs+3mzK8a8p2pST26G+ZaK+StuAhlyUTFmdAAAAAHD9wYQC6AR/GwIAJsDXV9F2Xlb0dZa9BCFZAPYC2AQBAACAnYNQDtiYP/klzlLz7V//lt0N4uhp8tVBz32pu9XmG2/KaYIPq6Y26WhofZWavC7mYdLeYpv1VbOx9VWNhYYn0E1TTiVCNrN2/YwP7Mzcv776Kn+fjkAFkbPDzA7jfSkWRDMjlMN2Hm3KkduTdhsZZiiXypQjDSxz+yKI/30etqOXi+p2lNGEA0RcdVYrs4pelw7l8Bh1WIP3t2XKMQI8+j7Vimh5mg7lyOOnwxhETfCpIqJ6Q1NOor4qt+qrlLGor74q9QZzyAUvH8qZh/2TATRJZYTS+iq0zHFxuKTnsduKKWdl11cx81t2fdXBHfetN+Wo1yK2Ufm7ce2ZYcqJ6qvkccuixdrIANk+X8QEAAAAwGhqTAICA5hyAABboadaeV2uwyQ5TDkA7An4GwkAAADYNQjlgI25feAm1H/0V1/Y3SDywhlvFreJ7nyO+5kO2Zy84m7ZlLM6d7cHt13wY3R91azHlJOwyEzB4Pqq5labcjisw3hTjgiN6En1Vn3VhWF+4fUZdVKpIMrqjGh+GO9LMRemnHmY8K/KEfVVeRxmqMqwHm2uKRZ2wERad+RxkshPTvkLNfxGpwwWEPNYaFPOQ3d7KOurRphyeHsWPth07v71hXJyVV8lDSZ8302CELJmLFlf1ZiJahG+4DFIdChnivqqvCCqmtCdPNej9XWEcsYElvy6+0w52wjlGKYcWV81P2ofrywLwZ0zDuUYQbko1Nd8LU05/nGUVXN8n0Q4RyLDTLioAAAAANwwMAkILGp1CwAAm7Dt+qrrMEmOkCwAe8G1MHMBAAAA+w1COWBjjuZhQveDn3m4u4EcP0t093ODwUGGbJZn4Xu+ZVPO7NBNMC/H1lfNu0M5l2HK0eEaxgdahppymvCDrNxKmXJ8fdUy/KwVGpH1VSqI0qqvOiWaHan6qrmqrxKWF8vsEW1P1leJgES1EoYZNtdwKGduBzW0oSXLjbBFV31VHcJB1YpaVV76jRBXrvHjWlfx+v3+zsKtNIxYAQt9n7PmOZqyPKUqgfj41GV43Dcx5cjQmg67MVneH+yqq5DZGBrK6Rp3edGEwsTzu7xIhLaM+qq+Ci1zXEPrq7ZxEbDsN+W0LhRmRn2VEZSLTDnN19KUI0M1lg0odTzCCsS2cFEBAAAAuFHAlAMsrsUENwDgyhDVV23j/fg1mCSHKQeA/aBGcBkAAADYNQjlgI3JxMTpv/+//9LuBvLGP0L0pq8Ok8XnIpRz8nL4WptyZodE8+MB9VUqlFPs0pRzEE9ka2SghYho0ZgvTu6H+0uyxooiQwZ6oruUoRxVX5U013D1UJYOTJimnJkK5XCgZ9kET7SVQ4Q1pL1HbrPS9VXSlDO3Qxcy4MO3Zn1VFi8nwx9Z3oSAZA1T4lh488o8fG/WV3EoJ6e2KacnlMM2nlQwJapSMiY7KhHK6TLO9BHVjMlaOBnQ0TVjPfVV1HOxbMgFr7o5v/JZOOfLpb2vPpQmTTlrVE3p80Kzjn1nKFXVfh1J1VfJ8NABh3IS9VVJU44M5QhbUlegKvWpRB9yM56XAAAAALjmYBIQWGDCCQCwDVBflQYhWQD2AmlABwAAAMBOQCgHTEKv0OAy+PM/TPQf/f0wWXzxKPzuiQzlND/3ppwDNwndW1+lLCT5LG0b4eV5uamZHaarq4hCbdWsWaZVX6VNOU39S1RRpawwLVPOhV3fI79nU4601uhwQ68pZy5COSvbBuNDCysXMuCf+YBEs298XDgw4MMePaYc3p62x/C6k/VVYt8t44u+8MLHm8eZrK8S45Fmlk5TTvNzNuUkA2WiKiqa6xCBlilMOTLwlKyv4rCFMKLIsfDXo+urOgIcbGKS9XSr8+56Mz3mvm20B9bcN2XKyYgoWy948uhFol/7+x1BJaPyjEM5We5eQ1qVUsKUw/VVhQrl6FCfN+XI8KOsr1LBo0EXCRIBMgAAAABcf/BfP7CAKQcAMClcX8VfT716mHIAAJcFgssAAADArkEoB0zCFz4bzAr1ri+AdZlyDu6FsI405SzWMOUMrq/awtNsdtATylGmnMH1VSJkoOtnosDOvKmvSlR0ybopWeFENNCUMw+BomIRJvx9DZU25Rh1WVF91dL9PDLliP0tZt2hiy5TTlRfpQIZdRnqmSyzkF4XH2M+FnWdCOWwKUfZgOpqhCknVV8lDDY6/ML7NIUpJ6qvkqEcEe7g463DUdEnsUaEcjiw1RUmYhNTPgvHqLywL+xbNqxU+KyLPlMO/26dC3Uf+Cmid/1XRI9ftH9vBbk4xFccxOeBNOUUc/d7fk1pPSd1qK/PlGO9PvSkPaOqtT2+iAkAAACANcAn84HBdZjgBgBcLbb5KcRrESTE/8cA7AX4GwkAAADYOQjlgEn4yEsh0HK23PEfdwd33K0033Bt09NvEfVV2pQjJostWqGcYnf1VW/+OqK3fWP6961QjjblGPVV0oDCQYloUr0JKOSzpu5pZYQleH08wW6YcnQgYnnWmHJEGKOYq/oqEfKpVoaZh0MuHMrJ3IWTvNlmeREvx4EBacox66uUoSXL2mGLqL5K1SfxvrfMPLN4Ob8uZcqRpiFrfzMrlJN4Wef79JlykvVVU5tyxHai55VhyvE1RUNNOX31VX2hnMI9BqUM5XTUV5mhnAnrq/h367xx1maw1raNyjM25cwO4tcBGaIhclayVCinFepjU44M5YiQT+dxyxKPqbQ64aICAAAAcKPAJ/OBCT4FDi6J+x/dTr0wuFr4/2tS70k3Xf81mCSPxo7XXgCuLP5PJDxPAQAAgF2BUA6YnPPVji9MeFOOUV/19BeEsI4P5RwOrK9SoZxiqClnC6Gcr/qLRH/hR9O/51DHYFOODuVYphxheSnmLqjAy6fqlaL6qoQpZ/nEja9VX3UQ9oWPO9t5rAAAURMUKtshjZUO5cwoqpMqFnbowj+GHLoxghFsAnIL8A/D/ssqJB1iaplyeDyyvqo2jq/YD3lcqzIEkTRsoGFTTpkK5YhQk3yjxo9/Jc6TTd7ISZOUfI7ogEtdt8NRUSin0UkTiU+w9YRyOk05Tegrn7v1VFVzrnecHzqsp8fInNwn+v63En3y/Wo94kJfCq5AGws/3vwc0FimHB/KOVSvA2qci9tEp6n6KvVc4XUsjfqqqN6uin/XhQx24aICAAAAcMPAJ/OBgf9wxG6HAa45T14h+qGvIfrwP9/1SMDWEdcbtvLCcg2ChDX+PwZgLxhzvQ0AAAAAWwGhHDA556tdm3KaUM6Fqq/KCqJ7nydMOVxfdTCwvqpsGz3KRAUQL0+0HVNOH0+9hehPfi/Rl/xp9/2MQznNBLo25RDXV8mKqiKERIhEtZKqr8qKts63ZcpJmDDqmujhp4nufq5hyhHBIj7uHLppmXI4lNPYYvh7H8o5C+vl8XG1Fv/cDF2oEI1l5OD9k9uTCmIOnVg1TL31VVW8fr+/wvgTmXIM64m/T/Pz8z5TjrTt9JhyNqmvknYY+XjK8yDLmseFjShG7Vdkysnav7e22fUptLoM5z9RCJ9Z9/GhNMuUY7zJffRZotP7RK/8gT2uLlPOujYYHULUWOcMm7VmizhcI802RC6UszptxmdUyln1VRdGfRUlXh+i8944nlHVGi4qAAAAADcKmHKAyTWY4AZXn4tH7r0gf+gJXH+2VWF1HUw5hP+PAdgPhNUdAAAAADsBoRwwCd/4pc/5rx+cdgRVLoP5LTfBey5COU9eJrr1OldttXziwhvSlDO/RbQcUl8ljR6JIIdf3rBoXBZ5TvRN/x3RnTeG72dHPfVVdY8pR/yuaCwi5YUdOvIhGmF6YYOLPGZPXnKPw1NvduvhcEA+U/VVos5JG4uIRMhF1T2xNenstXhcuk6qmNsBk0rYXPxx0m9eDFOLD+U0AaI8V6acxDnhzSvSlGPVV3HoSNVXWdYTJsvc8n31VVSHEJUOv/AYvTVnZCjno+8h+qGvdZVlMrSWrK8aEGYy66t6Qjkk7DsaDt9xOIhfF7rOD125JX8XLd8c81JZawbXV61xgYuNYXqbfkxV+5yRppwonKdMORyAJErUV1mmnER9Va4eO7mvyeufHNZas9oLAAAAAHsMPpkPDOSHIwDYFtciSAEGUTfXR7ZeXzX9qi8NmHIA2A+uw+sNAAAAsOcglAMm4bc++cB//RP/6uM7HAm5N8yL27H55uQVouNnQ0jj4rEy5dyOQzwWOgySF0RVRwCpVoGOXTPvC+VUKiiRx8ECtgLJwMLqzDaz+ADOilzII1Ff9Vpzrjz1ZnfrgzhzZ+ngr70JZ+nWqbcp66sqUV919LS7ffJSGDsvz2YZonTASpo4iBL1VZQOhbDlJp/FISDL+EIUxuDrq1TIiJHhosiUU3XXpeUzYcpJnLu+bixTF7VFJRefC2NNOX/4IaKXP+xCUvLYRmE3FXDhCi/5O33RR9eH9YZyKB0oqlZNiKzZFr+OmCGbjlCONQY+Xq2AjDLQWJiBsAFoM5hGhw2JQiinOIhrsyxTDqPrq1qmnGbsUU2gqJ/i22gfez6NGNVX4YI4AAAAcKOAKQeYwJQDLgH/PhnvQa4/W66vuhYBL7zeArAX1PgbCQAAANg1VyQtAPadL3lDmJz9N950b4cjaVgcO6UwI005RE0oR5pyjgaacsTkezG/uvVVFvNbzhJE5PZZ4kM5XaYcEUDgepuzB/b+ddZXiXW+9oK7bYVyFsqUI+qrukw5VdmENJrvD+667T55OYydKASOvCln1h2i0KGBKBRi1EtJJaivr7JCOeqNUK1DOQlTjqzhknVJ1rKSfCZMOYlQig+5ZGI/1P7yeTLWlMP7zzVkRHEAhsfI+OOtTDItU06iPqy1fTHeuiL62b9F9MH/N16GTTlsK1qetrfpl+XnizjmXaEcbxhSlqJahVMsfJXXSDaqrzpQgZcxppxcHe+e+iq+j7mPqU8lyrAWLioAAAAANwt8Mh8YwJQDLoPW+yNw7dl2fdU+n0sw5QCwH1hmagAAAABcKgjlgEn4H/7cV/iv7x3NO5a8JLT55uRlZ8rhUM65MuXMj9ykcZmq9KEwWc/ks44KIBKhg6sSyjkKX/fVV7G9JDJdrIgocz+//Qb3s4efTtRXyZBMExSxqode+4S7vff57pbDJvnMWTr4Z/zzatXU7RhVOUTumFerEJLIc6LDewlTjgzlLLpNObkw5RAZn8xN1FdVTeBBm3JSwQ1vylmE9fSacoQdpqu+iu/nbS0dphzKlClHhSv4PBn7aS5eTyUqsGRtGY+R8YEQbcpZt75KXiwqiX7jnxD93rviZdgcw8eRg2xdoS3TlLNOfVWXKacYf7yJghkpWV9lnDNswPH1VQNMOXmfKafZ91R9FZHaxwEXCfz91zw2AAAAANhfYMoBFtdhghtcfa6F3QQM4tLqq/b5XML/xwDsB7C8AQAAALsGoRwwCW9/4136J9/xx4mI6H/86d/d8WjIGRxkTcqTl4luyfqqR7Eph80xq9P0OnXNS18oR1pnrgKLW+HrliknG2DKEYaau5/rbh980g4daVMOCVOOnKh/7eNEh08RHd5138v6Kg7iSFNOuWo/DjxeIhfYqZZxQODo6RDKkaGfaiVCN/NEiEKFaKwKLn+RhtqhEBlIsuqr9AULH1SZh+/NUE4RbqP6qrK9rHU/ovS567cnTTli3LLmbF1TTl3Gdpje+qqu2q+O45/aPo+9XBGtRFilrt3YZEUbm13M+ioRYpNjTo0hGcpRBhqLbdVXWaYcfq2YLeLXAR0qi+qrjKCcZdqStYJ99VW9n0bksBZCOQAAAMDNA5/MBxYw5YBLAEamG4Ssr9rG6q/BuRRlcvZ4PwC47iC4DAAAAOwchHLAZJyv3KTrC6/01EBdBtKUU5VEZ6819VXNJLI05RQHwSKzTFS8ELVrk3pDOVewvooptCmnCWDIiqq8CKEUoibs0uz/nTe624ef6jHliPolK9Dy2sdDdRWRCM3MVX0Vry8VysnD78tlWA+RC+U8ZlOOCLNE9VVz+7HUIQRppZHL+PCA6hqvy2BdqVbChDNvr4eX17/vMuVkKpQzxJTDdAVquG5Mf2qLHxMOlVhBpi58mKeiuL5KjFk+dq1QjhWKqrofH4lcti7dOV2ex78nas7/5lix2aXLfBO9Lhhj1MtrI5c+zyzWDuU0NX6pUE5Vtc+ZWfN6ODtsKqWG1Ff1mHL4+FmmHFlfZel0MxEQk/i757j4t2989D1EH3rnrkcBAABgn4EpB1jUrS8AmB5UgNw8Uu9JN8WHcvb5Qyb4/xiAveA6hAABAACAPQehHDAZh/MrEj4hcjVVbMrhSenDu8KU89iZcoqFC3QMNuWIyfdinq4AIhIT7VfkuHDwiPdZwpPhPmTQhD206YL3n+urygt7/3wwoTGiZLltEHnwiTiUMxOVVUdPER3cddVW3oSzaj8ORO36Kh3K8fVV87B8XYWwQTFP1FeV5Kuc+Dj5n/uFKAoV8D5KE4yuy7ICSrx/RKK+itejPpkV1VdpU87QUE5HfVWWq/oqYRQiCkGWdU05kaWosEMt/HV0LPtCOXxeJ95g1lXYh7py56805XjzTR6Wu+ior6pEiIdZx5TjjTEdn8DLlXlmKPw6WHaZcnToKyeaH7vno7TQtOqrjsV9LFOODPU1X1901Fe1jDcDTTnrBpbA7viVHyL6xe/f9SgAAADsNTDlAAtMOIFL4FpUDoFB1NSuKZ90/dfAXFHj/2MA9gL83wUAAADsHIRywGTMi3A61bt+I7a4HcI4fHtwx/0jCqYcDuMMMuWU1DbldEySe+vMFXmasSlHV1cRGaGcWdt0Ua1CRc3swJmHeNnW+oTZxlc46Wqn2jDliPqqxTHR9/w+0dv/jKhzatant+lDO6ULPOj6Kg4kyDBLVCc1t9+U1MoiYgUuOHREFF+s4WOXCVNOy/iSqK+KTDm1EZpImHIsq451P6KO+irRmc4XhrT1abWmKcfXIJXi+ORxkEg+dlyr5o8b76u86GOEcrpMOVHV2SoOq2hTFFEwu3TVV8nnuK9ps0I5zTpa9VVbMuVUZRh/0pSTsCstbjmjVhTOU2abxZ2wfKFNOXnClNNVX5V1PHaWKQf1VXuLDCkCAAAA6wBTDrDw5wXOCbBFrkOQAoyAr49sgWsxSY7/j680Zw/jD0eBGwz+RgIAAAB2zRVJC4DrwFe/5Wn/9Q//0vM7HAm5WhVvynnY/Eyach45Uw6bWQabcmR4YJa2jRCFSeirZsqZHbR/Z4VyMmXmKJdxqOPO5zTLGi8jvm6qFOaVPPyMiOjkFRcYSNVXERHND91EfcuUo+urRAhI11cdPtVePwcNfC1TImBVKYtIsj7JqK/yQYtm/JUMlyRMObI+iSiMURtUFsdEh/eI7r1J1VcZVUQS+btkKKeiyA5Ui30pNjXliJCPfH7osBvjgyh1/Ls+U04qFFiVIVi2agJ4liknL8K+dplyahHikWNOLc/r18d+cChH7Nfv/Quid35venmiEEgkSodyUnalr/+rRF/555v6Kg5TKbONr6/K2uedfv3gdUSmHHWM5LaiiwSpC6Ay1IOLCntFXRMeMwAAAJtRm1+Cmw5MOeASuBZBCjAM+R54i6acfX7NginnavMT30b0M//trkcBrgKorwIAAAB2DkI5YDKKPEycvvtDL+1wJNSYclR91cGdMIncMuU0t6NNOYlgA1EccLkKDDLlKFOINuVEoZw3Nvc1JvRlnRSbV+TPiJwlhyhtypHI0E21am9T11dpU45ejzTlyHopTV3F2zIDF6n6KmGX8SEgFS7RF3W8KUfWVxn2m/kR0Xd/mOjL/0OjvmqgKUfWd7X2OQ/7JEM5fH+ubVvH3EIUB6KyvB12Y/i87KyvkiahrP17iayv4lBOZMqRpqhmHINMOUNDOSlTjjLQWGRZPIYPv5Poff+QqOx4DeJgIlG6viplyvmG7yL6kn8vYaFhU07zemq9xunXD34+lOfpkI+uvOqq84runzBPgatLXWECFQAAwGbg/31gAVMOuBRUzTO4vniTMG3n/51rEfCCKedK8+Qlosd/uOtRgKtAjf+7AAAAgF2DUA7YCr/6/Cu7HcDitpv8LZcilHPXBVOy3E1WR6acxiLTa8oRk8/FPIQTzOVV5c+u8aEcw5RDmQrlFO0J8qqMwy4cyrH2zzLlaDtMVyhHT/IXor5KPw5y+apqm3KiUM48LB9VaxV26EIHYrTthygOhVh2mSwX2+PjK+qpJBzy6AvlELlgTpbFdphUwMLvvzpuZqisjtdLdTtMxNaVsW/kePmqDIGgvIi3V3SEcrwpR38Si0MdHRfL2MzB5wbvgzTIyPARP0ZsdukK2fQGt3j55nivVV+lno/nD933j190wZxf/AGiswfxfQaZclTwzNxuIkSzOHa3OkTH+xK9fohzje1DrfoqcZ9IlJP6VKKor+J9AXtCjccLAADAhmASEFjgU+DgErgOdhMwki3VV12HOhmYcq42ddVteQc3iGvwegMAAADsOQjlgOsJG3EuHoeJ6sO7bnKXLTpDTTmf+S2iF3/XMOXMu+t7/ET7VQnlNMGjIlVfVStTiGXKEfvi66ssS4asGVIhj0qZcu59frgfT+63TDnC0FJXRiiHgxClCzwkQznN/ThowGaZLlNObgUu6niZTJtydChHmHmIRIhAvRHiMRQiZJQK5bTGxKacMaEc4425twd11Vc1oZLR9VVluJ8OovDYtHXGPG6J+rCuQIwP9qgKrtKor5IhsiXXVyXqzeS4ojF0LK/tNoPrq8R+nTW1fI8+S/Sp9xO9+/uIPvLu+D7nwpSTCuX0BrmKEKDS4zy40yxjhXJ0fVUVXnuWOugkHr/ouA015fTUloGrh6ylAwAAANYBk4DAwv99iXMCbBHYBm4QqK/qByHZK01ddX+gdF1+88eJnv/F6dcLtse1eL0BAAAA9psr0qsDwMTwZPH5o7i+iqgJ5Twabsp5x/e4Wx1KyYvuTxvskynHCuVkOVEt9q9aqvqqJpRj1ldxAEfYaHT10OMX3ZiOngr386acRCiHDRs6tMPr9iadnlAOBw28KWeWqCcq4wqdVH2Vrk+iOraoSDMPb5/IHfPf/b+JPv5rRN/8Pw+vr4r2PYuX7TrfigGmHG/+EfuiDT8cZLGCJ134N4BluK+3pHC1mAzlsB1Ghy90KCdP/15vm48BB/BkWEXWtxXKlDO0vqrL2pIy5UQX+hLoUM45h3I+HfZBX2i5EKac1jbFmLrOmWi76o0711fp84ooDvMQucf74A7RyXl4HmvzjlmV1UXHeQGuNjLsBwAAAGwMJhdAA8IS4DK4FpVDYBCyvmor678Gk+QIyV5t6iphyd6Q9/wg0ef8UaIv/HemXzfYDqj4BAAAAHYOQjlgK/yJL352twM4bIIep6+1QzkHt91k9VBTzumrRA8+addXdb2xqUsiyrb7Bn4MbMrhfZbwxLs25USmi5UdysmNwIhVX6Unzi8eh0l9xptyFmp9zXafvORuZdBGb69cEi1uhd/JZTk8wEaOQfVVPdVENZFZnyStIhwu0fakuiL6g58j+tA7NwjliDFVZfeyLVOO2Od3fI+rI6ortz8+kyNNOaq+aqwpp1KmHGnk8YEpEajisFhkaMk6QjkiSKTxgSiur2qe65YpJ5+F8bSsLmQsP7K+Sof5tPHFQltk2JTz8DPuuaT3hUjVVxmvbUT9diW5XR2iYSOZZcvS4604lPNy+5hG9VXWRYLMvrjn7Ueor9pLcMEWAADAJmASEJjgXACXAIxMN5DEe9JNuRYBL5hyrjTbCuXAfrt/XIcQIAAAALDnoL4KTMpf/ca3ERHRL/3+y3S2HDlhPyW3nnG3p/cbo0RGND92P/P1VQNNOeeP3M+1KcbbTxJ/zOoQy65ZdJlyshDqIGpMObq+qoyNGHfe2NzXMuXwJDlXD2WiYopDOSdxeKqxonEAACAASURBVIYohFG0ecOHcl52t61QDtdlle5xkqEeaeKJTDll86mnwqjNobA+GZRImlqM+iRpxcmbGiZ5fN2CzibCb5Br9fuxoRwdItLwevl8l2aVj/+aq0GyQlR+X7hG7Dwe71B4PdWqHSDi8yMKuDTnpQytcIDs4/+S6MUPjDDlqGPLAZbIlCNCNhzeYasL1e3nul6nHEPVEcrRRhsfTukI8GmLDAduHn3GhQaJ2mGfqL4qZcoZUl/FjzPvfzNODtVZ9VU66MamHKJgH9Lry7L4Pr2BRphy9hZcwAMAALAxmAQEBghrgcsARqYbRE2RRXjy1V+DgBded682db2d+ip5rRDsCfx/F56nAAAAwK5AKAdMyvf+6bf7r9/+379rdwM5akI5J/fd5PXB3TDpf3DbmSVWZ8NMOdI2EYVymonolC2kb7L7svH1VZYph0M5K/IBGh1UKRP1VdY+dply+HgtT0JQiumrr3ryh+6WQ1d+/MqUk6yvmof1DTXlmBYUuayor5KmFhm0aNVXidANB1R4/NHvOTg0wpRjmYv8/jfrPbzbbE98Wmb5pHmjznpmY1+8ZaYJeFjBky50fZUMEPnAlAq4RIYhEcp553cT/fzfofj4D6mv6jDlyMeHH3e2uhC1zxFZUSbHnBpDqr6qVuEUi8giQ6K+6jNEDz7RrFeHcprXrsN7IUjV2m49wJSjPk2TqVCOVV+VadNW6V6Hidy5Fq1PVJglP7nTYcrx48eFhb0B9VUAAAA2BZOAwATnBbgE/HuW3Q4DXAJcX7UtA/a1CHhNEJKta6Kf/z6i+89PMiIg2JopB+/p947rEAIEAAAA9hyEcsD1JDLlPAqGBiKixZ3GlHPeb8qpyjCBTBQHNHzwJPGJgz5ryWXj66ssU46or4psMuINljb/HD/XBFqMCfnI9FKHCieiMFF/8djVJUl8fVUilPP4RXd7pEI5uQj8lMs4JHBomHKyQthasnaAwO/zQFNOhLIOZUVYvxXKqcvYlJPPDPtNT1hDrqvTlNP8jsMR8ty9OHHf+8fLqOLix4VDJWNNOT58ZBiAeNyFrq+SoZwsBMguTlwwJWUq0uh94ACeZcrJ8rDchQjl6P0dXV/V3D9pyukKX4narqqKTTmvJUI5F80yt15n11f5EFhXKEc+N1R4aLZwQTrzNUAE3eraNuVoQ5AMAMntJM9/1FftLdKABQAAAKwFTDnAoMZ5AS6Ba1E5BMaRbecl5TrUyUwRkj19leg9P0D04R1+uPO6ss36qn0+b28i1yIECAAAAOw3V6hbB4AJYTvKyatEZw/iUM7BHTeRneXBGjM7IKKsbcq5eBx/LyefedI+9eZmr0w5ORHVcfCGTTm//7NEv/ljbVNOMSM6fn2ivopDMqvYRkMU/vjvqq/Sk/w+lPOSu9WmHF1fJU05s4Uzelw8DmEdDhzJsfkAkQgAcL2V344x+a/vk2VxkIWDS7LqzK+nbmxCHFZZue3J7Yytr+qsImJTzr1meyJksjxxYZua9czClKMNPhzKSVmiUkhTTqXGmjTllESypoiDG+XSVUtFx0cEiVLbzrUp5zw8hj5kMwvjkKYc/ca1VUdGA005OsinjDEWWR62d/Eo3OfhZ4gefqpZrzLwnD92z6mDO3Z9FZ93XduVzw1tyiFywTqrviqy3jS3/Dq8TNVXJWrkUmhTDi4s7BH4VB0AAIANkaZBTAoBD0w54BJAKOcGIeurtrH66zBJPkEY0n+gZ5+PwxWFr59Nv2L8P7tvXIcQIAAAALDnwJQDriezA1eNxKYcrushIvqqb3N2jJOXgzUmy1xYRZty2EbBNUtRfRUHFFKmnLJ7svuyGWTKKZUppyR6/heIPvBTRJ/5zXZY5u7n2NU1/z977x40zXLf9f1mZve5vddz1Tk6ko4sy7pYloxvKcuWYwfLlimXC1zBFS4VCMaGYAhQGJwQQgCjECeQckHiFIlDCCQBiosNGMd3Uxa2bIID8k1CloQtyZLPkc45Pu/led/neXZ3Jn/M/Hp+3dM903PZ3e7Z76fqnGff3dmZnu6e2Znu73y/g+OrqrKxOEetr/r+eSXKkZFURCK+ak20WTe/z8ubgiMlyhHONRKzDa2CCzNeKinfK4QLiRQByXKo+KpKrMFCLlVXPUU5prOPiSu+qihK0dKGRVTCnlmKMfj77C7T1ylHio9MJynZ79R7LAjh+k5qAdnmsuxDsn5UXbSJcqr1SwELH8NSZGMT5TTiq9qccmxxaOyUY8ZXGY4xNqTI5aKKrlpeK+2dWTxounZd3isFadmxPb7KyylH1KnN0efohvscYMaycb+7csVXSacc2YauyTZDJISBhXgocsLT6wAAAMYhryPwmwIqzPs0ALYBIkAOCzU+soX2nkNf0pxyhq4DopytsVWnHLRXXBTGXwAAAADsmoAUA2AuvP11j+27CCVnj5YWqGZ81eu+gugPvofotV9G9Oovrt9fnjSdcliU85pqOZsox+mUs26KWPZJl1OOiq+qJug5toYn0K/uN/fnq99N9BX/pWV9IqpKZXCbTjk94quIKreZVSkwWBoOO1IEtLlqigQ4woodPVgw0BAMGW1Z5HU0FpEulpHLyCenGpFLlRNPvhZCHS5fUS/LAhwzvoqKDrGGFE10xVdV2+XjgcUo68vq6ZmrZp3IfVHxVZXAY6hTDrsDyf3iek5t8VWmU05RlvnqAam4LSIhzrDFVxX6Psg4J3N/0swvvqrRnuTnlGOKZ7ziq4Rg5bIS5TzxBn1djfiq+2VbL471mC6z/G19RoudM5xtiIiOr9vPc9L1hv+q+CpTlFOtL830eut6ILHhPIUBoWiQYj8AAABgCPL6EL8pgJkiRgWATubgbgK82LYr2yxcl+CUEzRF3hyDmmq9EHfEBZxyAAAAgL0DUQ6YnDe84vq+i1By+gjRg5fKCWwpyiEievz1RP/JPytdc5jFqdsp57VfWv61OXtEF1/V5pQjhEQsXJFOIebk+2vfQfTs25vrkyIZFpWYYoU+8VXyvbNHmyIVKfgx46uIiE5ZlLOol2enHHayUeUVmM4zNsGFK76K15Wk+vaIdBGBcqvZVPFVFkGMt1NOV3xV9ZmKr6q2zW2cr6gWAYn4KtPhh8UffQdMciHSMI8PZ3yVFOUkIr7qqhSdsLMPL+8qlxLcVH1DCvA42klzN2KnnPN6OZ/4qjaBCC9vimcKi9jFRIpy2Cnn8Tfqy5jrZUGiS5Tj45TDYql80xTREJVOPLb4KptTzlH129AaX8UD3D6DBGZ8FQYWogH58wAAAEYDpxzQBfoF2BKzEFIAP7YdXzWDSfIpxJBwytkeRV66Ym9jvWivuJhFXB4AAAAQNxDlgMnJ0kC61dmjdXzV8c3u5a1OOdXk92veTvT2P0L0+q+sP1OuIW3xVSGJcji+yuKUw4MM68taGGM65RD5O/9oTjkcEWWKcs4t8VXVtq1OOdV7p49aPpNOOevm91V8VVbvBzvXsGiGyysxI5Z84qsaQpJK4JHbRDlCvJOvRXxVJYrxEuUId5iuyDTllMPxVdW2uY03K+GUI+OrDKec9VCnnKpObLFcXM+Z6ZRTkDbpklT1sub4KotTjm0AvrEPNqecdV2W1OKUk5uiHF6+K+LMWN4ZX9XWdlndP1ks+IQpyjHWq0Q5J83P5HbbjmvTtal8s/78TV9L9IZ3Wb4nyst/FydlvbqccpLE6FPifWubGvFXmHiJCHFcAwAAAEOAUw6wAaccsAt6PUgQAPmG6J//t+VDa2AY246vms0kOZxygmNr8VUF2is21G9WJL9dwM4vfg/Re/7yvksBAABgIIGoJ8Cc+MZ3vFa9fnjVc9J+Sk4frZxy7jWdcmwsTsuJ+s2a6Mf+ItH5i0SX98vPTm4Tveu/IXrsM+vlO51yOlxLdo1yyjlqfsaT2hcvE51Ugo00K/dh9YDoiTeX3zdjoVyourFFRFWOG6sHzfiqpz6H6OnPbcZTcXmISrGV67N8XcVXOUQ5mYyvymtBh8spxxS5WJ1yjPgqqgZrlNAiqQUVprMKR05x2aWQi+N/+jjlFB19jrfLcV5sYcvOJZtVdZNmOuUYLjMs8Oh7A+7aV1k2Wf5ECJPKN8r93azK768vqn3wcMppuP0I55i1IcpJF3U55HKmaCuv9kFzSrJEnKnlWZQzNL6qunFW8VWVKGdxSnR8q3kuurpfutNkR7oIySxP63aNKDoifX/f8ceJ/v0/6SiviCsjKuv06Kzub/IY4W0NGdRBfFV8YAAPAADAaOCUA2xMEKMCQBexCSle/DDRT3wH0Ud+fN8liQ/pjLzN+KqYmcQpZwaOQaFSFNuLr5pD/z0oIhOUAjsf+D6i9/2dfZcCAADAQDxn2AHw51WP1IKK5+9e0Gsfv9ay9BY5e5To/NPlBLC3U85Dok+9n+hf/BWiRz+jvlC1iXq6RDldriW75uQm0dljRI98RvMzJcq5U+8ri0JWD4muPV5OvN94ym9bWuQNi3LE5P7qIREVzfiqN/6W8j/rOqv65igqrfxi3db4KnbK4fgqKXhJ9PgriSlyUaIPIc5wxVfJKKREiIZkOeQTK/m6GR/WO77K0ymHhVe87atKfKY55fB6LfFVLGIxRSpdqIEWi9iI+4wZX2XGJiWpHsV0eV845XiIcky3H6JaZCRFUza3pka82bopgnL1JV6eyO2uZcayaZ/J+Ko75d8n3lT+vfUqooe/YXfKeeS1VXyVxSnHK77KiKIrC+NevvG9XK/X45t1/JY1vio3PuN/tjnlQJQTHYivAgAAMBY45QAbcMoBuyA2UQ4EDyPYcnyVmiSPpC9ZmUAMCaec7VHkRJst1Cvu6eMjtt8u4AAPuQEAQMxAlAO2ysdeerA/Uc7po7WjRB+nHJ7wPn+hjlM6vt5cvtMpZ03ecU+7YHFM9K2/bJ+AZzHAxctE154sX6dZeWN8dU5085VEb/3t/tsy46vY4YSo/DfH1xxZ6tVF1hZfJdoit8RXfcaXlWIrbk/eNxWt5XDKyc2IJd/4KumUI9bPognVL4raUSXf6O5KMgbL2ymnIzKN180iNRaHcETT5qreH2t81UIsR/3jq1RUl7GvREKMJOOrhDCJSIhyHtbLrB+K+hFRXiZKWGSJrzLjuNLMfuwWeem+9b7/u4yzsx3jSUsZeP1D4quShGjD8VXVee3G00RHN4huv7oUVplin8v75bkvSe1OOSyqauszWv+yOOU4vyfPAbydtBQGPnih+syIn2KxnFoHb8e1PaM8GBCKCMRXAQAAGIsZYwkAEZxywE6ILQIEE7Hj2Vp81QycKyZxyoEoZ2tsy9EG7rfxEdtvF7BT5HZ3dgAAAFGAESywVT760oP9bVzGHJ30cMphUc6DF0qnCaJy4tuEhR8u14t8E1Z8FVEpqrBNqFudcqrIpdUDe5xUG1LkYouIWlWinD7r9YmvYnGFKcp5/TuJfvc/MGJyWJSTifgbM77KdHMxHDnUDY0lvioXggclylnV7/F6pFOOdFdSLjF5t4OK+n7eLgRTTjm3qu9UZeQ4oXwltifjqwxBi4qvGuOUY0aDZXoZiYRziph0SVKilSEwaTjl2FxVeB+MvkLU3J80a7ot8ecf/H+Ifvi/Inr5o/b6trkpMaqtXfFVbe0sop0u7pbbXZ6WzlKvf2fZ5xuinHvluSs71mO4VHk8nHJsLjQ+k1/SLUtu59rjpeBRrpP3O80sx1YLvAziq+IDA3gAAADGoomFMbkAKuCUA3ZBbM4zcCEZjumMPPn65yCYglNO0BQ54qtACdprHuDYAwCAqIEoB2yFk2XZtT724vn+CiEdVbycck4Mp5wXS0eK5VntECJR7iwOYUKXa0lI8CDDwzu1gEk55Txoxkx1rs9wytHiq4ramaXPelV8lUWUw+tmNxCboMJcF4sFZNnMtmwIRwynHNPpg19r7i5pXXYWf7DoRcZccXm0iK2iKQxq7Hv1GYtMFkct+13VSyO+6rz+N7sDSfcRVuDbop/6qPPlvjYETwsiSmoxB5EeM1a+0XTK4eXkX6tTzsbYB5tTjogXswlV8k0tfNmsyuXNtmkrg3LKMUU5HrFQMtrp8m7lgJMQ/YffTfT2P1y2rRxoyfNS/HZ8o+wTtvgqH6ccM4quq5yqvDannIzo7HGiBy9yAfT1afFV8n3XZBviq6KlKOKZxAAAABAoiK8CFkxHUwC2QWyinFkIP/YFx1cl22lvV3xzTMApJ2z4YcDJ+y/u6eMDwuVZgIfcAAAgaiDKAVvh5kk58f3d/+JX9leI00fq1z6inOWp7pRz/unSacL1XSXKcTnl5O0OFCHBQoLLu3W0ETtzrM6Jlj0jyFTd5EKUIxxEhsRX8TqtTjkpESVup5zG8hmVbjZrw8XHiCIrjDZsOLFY4nwS0sU2aSZEP+t6PUlS3Rxv6s/ytYivEtFNXqKcSqiSHbfsd1WHXO/cd6+EeG5zRbroQjrlsLhI9Pk+bjm8HiVAMuKrzHZLs7oPEVX7muiiIPW++GsV5VRtxRFm0m1nY4hykqxsH9MFhyPCiMp6kiIqWWZXGXj9g+KrhGDlQhynTHakr/fqfvn3+HotODRvullQ1eqUI47bPvFV0hnL6ZRjia/qJfIyvo+b0niQDlgAAADAEEzHPACICBNOYCfEJnKJrbwhsrX4qjm0zZROOT3dmEE3chxu8vXidzYq5Hkm6nPOgQNRDgAARA1EOWArXD9uidDZFVK8cXyre3nTKefBC+XEtkuUwwICU8jBSIFF6CgxQFHv7xinHClyUfFVYuJ8UHxVVd9SbGVuc/VQX9aFFMloLj7GTWpuOuUk9ftEDueQarBGCgZMp5w0q0UWjfgqFuUk/UQ5LDJZtIhybjxFdOvV9TK87ZWImdtcltuWQoeGKEc65fS4sWfBRWFzykktApeF/kQPi7vWZnxVov+1Ye6DXAeLjJR4ZKH/levgfWCnHGd8VYsop/GUksVxyYRdg4hK8ZwZyZctiDbiXMSinKNrlVCrsIjO2MGmbbtSZNRjwMV0y+L3zh4rBWRX5+J9m1OOz7bMyTgMCMUDBhEAAACMRTjuQXwBGM2xAdcaYEvEJqQwHyoC/uwsviritpnEKSeyYyomlChn4ggrROjEByI+Z4J4EBgAAEB0QJQDtsKffNcb1es839OF3minnBf9nHLMKBomqvgqcSpQTjlpKb7IV/2dcljUka8r8YWYdM+lU04fUU5Vl7b4KqKyrllo0emUU+3vZmU45Rg3lEWut6EzvirRl5EOOImIZFLxVewcJCam843uriSjm1pFE9W2lVNOS3zVF/8hoj/0U6LvGvFVRGXMkRlfxWVU0U/CkWWoU455fKSLFlGOEG8kqe5yQ+TplGMKi8Q+qPgq4ehC1BR35RtdWGO6/cgy2MRK8j3NbcgQp9hIs3q5y3tNoaHplKPizE5qEZbpMGTur2u7vOwgp5ycGk45RFWEVUd8ldqOY7JNlQfxVdFR5BgEAgAAMA7NMQ+/KYCZwLEBgE4iE7lA8DCSbcZXVeuMum2mdMqJuR4ChevUNXY9Zr1or7hAxOc8wLEHAABRA1EO2Apf9dmvUK/f+5EX91MIzSnHQ5Rjc8ppFeWwU45DlGCbsA8VTZQjnXIqAUJfpxyisn7yFRGJJ4s4kumqcmaZKr6Ky+srylFOOatSMCMjeiSmIMac/LeJKZJEF7IkIr5qI+KriOOrpMhDuCuxCGNKp5xsSXRyqxnXJZ1y1hfV/vA+yfiqql6HOuVw/bJTjBYNlrlFOdJJJklqAZL6ruGU0ybKUcIi6ZRzVZeLSG8Ds/wyvqpoEeXYBuykU40U0HjHV1XrvLA45ajjjddfvc6O3KIc5ZTTFl8lXaQMEU0b8piS9XpWiXLOXyBrfJXKku/jlNNS5yBMYLcLAABgKuQ1EgB4ChzsgtiEFBDljGDLQr9eTrGBMolTTp9xANCPqk5dLu+DV4t7+viAm+AsgCgHAACiBqIcsBWWWd21LlZ7stQ7vlVP9vo65awviC5eLv+9ekB077naOcYkFcIOG7E65fBkvyz78rT/OrOjUoQiRSU86T4kvorFFC6nnHRRC1O64qtSIZKR8VKmwKTIDeGI6cRiESmw24eMBjLdWdgppyjqbTbiq1Jxod0iguAy+TjlMKkRvSadcjY2p5yqjBlHPwlxxxCnnGJTRYOJ/XI55cj4I6qcckxxSS+nnGrfpdsOr4/3hcthirukiGpMfBWRft4wxSk2OM6MiOjyTvO8lC31+CoWTmVHdZ/YDHDKkSKjQU45QsjE8VVEpVOOuT7pBlR+IF63OeVAlBMfBUU98AwAAGD/wClnHEVB9J6/Ut7vzgr0BbADYhO5xFbekCiocuzdUoTVLNoGTjnBIsdIJhflwP02OuTxhbaLl6JoOv0DAACIBohywNb51RfPuxfaBmlaRlglKdGRR/zS4qT8e//5+r07H3e7uWSGsMEkz9snu4NCDDBIpxymb3wVUSng2Fzpopw0GxFfVYkfTm/bP09S4ZSzsC9jrosFKKl0AxGYwhHTUccqpkhId5fJmgIuFr1I8Y4Zh8RRX4W5fst+E/k55TBKhFSVR4uvuiRlz0ykK/CVU44QlPS5EVACpLwpWkuzpgjGjNliMdPKdMrh+hFCIte2uX41pxyOr+LtsFNOtX3pisXrYVGOKbzzFeXY4qu6xFe8bZtTTrY04quq14vj+tzmcsoxhUUSKa4hD/GQKq84pmzxVecv1OtzxVepdbnqBfFV0VLkmDMDAAAwEnFdgomF/tz7daIf/4tEH/yBfZdkWuCUA3aBGWcdOrGVNzi2GV81g7aZ1CkH9/STIusT8VWgcP4DxASOPQAAiBqIcsDWeff3f2B/Gz99tBSZ+DzVwm4w954XooV1S3wVCwYcNzYyiih0ZP0cW5xyhsRXZUdNUQ4PmnN8VR+xT5qVZXNFU2nxVR1uMSpOil1hpPBAUOR6PZiCC2d8lbhATtK6PCwmSbJ6XSq+qnIU4X7FdeUbX6WccvqIcqr9lfFVqk54m1JgxH1+Aqccc7+keEm+x2Uiqp5QS3VBDVE/pxzlGmMRsOSGSIXFOHxukAKTfFWJqFxOOZZ6kf2rd3wVx5kVVayeKco5MuKrqvVnS6JFtc+mKCcXDjbO7Yr98REPMZ1OOS8091sKj3wGCHjAL4UoJzpgdQ0AAGAsmuMeJhZ6Mwt3Bgva/qBfgC0R2/ETW3mDYsvnkTmIcuCUEy6yPqd0yikq51u0V2RAuDwLIMoBAICogSgHbI1HzjoihHbB2aPu+CkT6ZRz+9n6facoR7hn2Ig1vorrKxXv9YmZYtJlFckk4peSjFR8VSrEAl7rW5TOR22fs+ilM75KCGKkU05DlLNpCkeIxMWvK76q0AUP3Icu7lTvJbUrCDvNsFOOiq/K6gvtNlFZwynHo04zITojqkVSRKVwoxFfVe2nzR2qz41AYeyrFOEsTsv/JKajD9eb6ZQjnVZcZVKinGof1hf195RTjuGmw3/ZfYgdfohK0Yu5D51lkKIci5ivq52LvHQ1KjZNp5x0oa9TxVcd10ItZ3yVh+hLWhP7iBylmIf7eJoRndwqj8/zF8T65D6KgQG1ncQxtmc692BQIRqKnNBeAAAAxgGnnFHMdpIeE05gB6i+FUkfm+3xvgOKQsRXbcMpJ7K+ZKMoqNW12Gsd6KNbYauiHKKo++0hosVX4ViLl6LfA7IAAACCAqIcsDW+7z97x76LQHTzGaLrr/Bblt0w8hXRY59Zv+8U5RiRRCa2CftQ0UQ51f5qTjlD4quqOJ2iMOKr1qWwoO86b76S6PE3uD9PstoJpCu+SjnlrCrXGkd8VWFEkCmhQaH/tcZXic9ObpWvH74slnfFV7FriHDc8XLKYZegHk45LOK4ul9/tr6sBpxYEGFxypG4RGk2lMuMxSnny7+N6Ld+V3s5qRLlmDf+Xk457KpSrXN9WUfTKaecapCC2zwznXLyehmOr3K5+wyJr+pq5yInurxb/tvmlKOJclb1+yq+6kr/TuHhlGOLr/JxylHHlLhZZLHX2WOlU44pqnHFV7lQxxiccuIDT9UBAAAYiXYdjkmh3szCncECohnALohNQBBbeUNlK+dLHluKuW3EmB+ccsJia/FVc+i3B4h2DsM1UrTAKQcAAKKmY+YagOE8eeNk30Ug+i3/fTPqxsVClPex1xN96IfL1y5RDk/Yu25sYnXKYQcOKTYY4pTDIgEpvrj2ONH9T5WCgr6inK/9zvaLThlf1emUIwRVSSqccwyBSW445bBzhzlgkFiccgrhQrKo9vVCiHKSKpOchRrFRu8zSUrWmCcT5ZRTucf4OOWkhuONFl9lccrh/bVFh42KrxJ97LHP1MVwREKUw/FVKVkFUIkQMpUbcG9biXIuSuelq/u1g4xqs4X+d2GLr1qTFjemiuToS/wd9domymkRu6SVy9RFJcoxnXJYBMewQG1xJOKrjHOh6QxkQxOssSNRz/gqMxbs2uNE5y+K41k4HakseZ8BAsRXRYt04AIAAAAGAaecURRznVCDUw7YAbGJ2mIrb1CwC4zHPfCg1c+gbfhBvGIzfJ6/gChnK2zNKWcG/fYQ0Zxy0HbRgjh4AACIGjjlgK2xSOub1nsXUyrye3D9CaLbr/Zbdimic24+U4pKiNzxV6kRAWSS5xE55Yh4KRbgTOKUs9Kdcm69iujOr5XxVX2FPosjomWL0EuKcrIOYYrmlJPWbdlwyin0emg4sVicclhsI91PWEDx8Dfq99gJR4o88rUQhMj4qi055cj4Ku7n60uqHWlIV+DbxE59nHKUK1Du5ySVinYiquOrGHYg8nLKMeKr8nXlInNcC1i4PrjNuZ643+Ub4ZRzVcWf9YivknUlBTTqZrgrvqogurxX/vv4lv55tjSceDziq3yccrT96eOUI+OrDAcidsox47Aam6xFTAAAIABJREFUTjlSBNQitEpGWmWD3cMZ9AAAAMBQ4JQzjrk6Z+ApcLALYpuQnq0IbwcURaXJ2VZ8lRmNHilwygkTTZQz4bj8XK8hZo8ULqPtogWiHAAAiBqIcsDWSIUo52f+3Ut7LIkn0inn9DbR2ePla2d8leE2YmKLtgkVvoE+vlFPcI92ylmWN31FXq/z5jNEdz8xLL6qiyQjWrEwpcspRziwJEktSjAFJsXGcMExHDlsYgpTbJOkteCFRTlpVk8gyEgnKVRhgUKnKKfatnLK8RHl8P6yU855LXChwoivEvtr68+94obyeruyXzjLacRXJamuBzl9pH5f/rUNjioXHNE30kUpWGGBjOno0nDKEQKlzapqL9MpR8Q2mYyKr6r6FbsaSREh75cU+qj4qmXdJ9aGKCdvaVe13qxe1hTRtCGdckzxz7XHic4t8VUsRCMi8hnMQ3xVvMBuFwAAwGhsMbLAm9lO0sMpB+yA2CakYytvqGzjnDKHtpEP4g2tI/kAF5gOLb4KTjkHj3aeQdtFC4+n4fgDAIAowQgW2Anf/Ld/dt9F6EZOcp/cIrr2WPnaKcoxhA0mMcZXSVcgzSlngCiHRQJSfHHr1UT3ny/FKVOLcrT4qo5kvtR0ypERPYLCcDsyXVCsIoXqCSoltMiIjq6X7z8U8VVUiSwK4ZQjI52StBI0+DrlsDOKR3xVkpR1pJxyzolObuvrlO4jpsuMpI9Tjnr6aeN3fKiIOEd8lRLlCKcVog6nHNE3smXpwKSccgxRDm+fnXKk64sS5ZhOOVxvNqecdVNoVC6sl98G9wcltjHamePiGBVfdewW5fRyytn4lVN9TxxTpvjn7HGiB674KlFv8rhqu9lEfFWEIL4KAADASKRTDn5T+iNjZecEnHLATohM1IZooBEgvqobIcqBU05YbD2+Cu0VFQWEy7NgFr8bAABwuECUA7bKFzz7yL6L4I90yjm51e2UowQDDgtQn3ieUJBOOUwqTg/LIfFVR9WTGDK+6pny7wsfGua+00aSicieDqccLg+7GSXCDUSSb4xoqj7xVcIpJ60irOR7LEDQ4qssTjnaAEfLvqwrpxwfUQ5RKQ7hvnv1oHSHkvugBp3Evtjiq/oM5CunHA+xEZeRSDjAGPFVDVFOS4yRLYIrOzKccjhmKdWXZaecXLbXyu6GpYlYDPJ1vS4tvsqIYbKRVC4y/D2zj3NcHKOWO6pFOXKbREKE5NG/tKcwJnDKubxLtGaxlRDlWEVeju0VxvGHG9J4QHwVAACA0SC+ahyRiQq8wYQT2AGqb0XSxzCBPpyichLednxVzG0ziVPODOohRGR7TBpfNddriJmD9poJOP4AACBmIMoBW+Ur3/zkvovgT8Mppyu+qhIMzMkp5+Rm873sSHcX8SVbCKccFuW8qvx78fIWnHKkA0qHMKXhlOMQUkjnGqKm4MI24CBjp+S2jm/py/BTvdx/8rUu8kgyPQLLBX/G0V0LX1HOshJMFGUk0olZPumUU+3nWKcc+USuj2jNdKPSnHKSur9q9ZN0OOUY8VXSKacw4qh4+1annKvm8rIsLqccPs/0jq9iEZfLKWepD7JIUU7GTjkX+nd8nHI0cU2P+CoZKWWKf84qF7IHL1TLpvV31OCOz2CeEV8Vy6A40M+RAAAAwBDkdQnEF/2Z6wQoohnALojt+MFT9eESm8DLCpxygkWLr5pSlBPZORBUSOEy2i5a5up2CQAABwJEOWCrvOWVt7oXCoW+TjnsouES5eR53E45PNEtxUp9yI5KkYAU5dx8Vf355KIccTrriq/ifdtcVUIPRxRZ4XLKMVTp1vgqw8XDFDwlZnyVEemUJLoYxbkvplPOsXtZSZqV7bN6WJZXOuVQQloMkykwIqr7/y6dchLhlLM4rvuQ2UZtohzNKWdZOeVwfNVaF6io+Kqzeh28v5t1c3mi9iilPK8FPlJAY/YVG7xfKr7KEEipuLhqXdb4KpdTTsvxImOo1A28j1MOu1EJpxzeDgsezz+tr6/RdtIByeZ+VL2H+KoIQXwVAACAscApZxRznVBDNAPYBbEdP7GVNyh2FV8VcduwmxC/HrQOiHK2ghZfNeEEvukeDuJA9gdcI8XLHH43AADggIEoB2yVL3/DE/sugj+mKOf2a8qomeOb9uXTlIiSFlGOJdomdOS+KpeQgeKZtIrT0Zxynqk/30Z8FdMVX8XiABbdSDcQSWEIqxqT/7b4KiOWSgmeuG6TWlxSFEKoYomvYuFG2/hPwynHV5SzKLe5elD++0TGV6Vio6KMUtDCx0ufG/tcCpDypqDFVkaiWoginXKy47pvNuLD2kQ5hrBocVSLVXLTKad6LfdVi68a4pRT9XtbfFVbQydp2V/b4qu4jERCvCPjqy6b5SFqP0/Z4qt8xiOlmCc3HHnYKef+p6r3hfhGibz6OOW01DkIE47mAwAAAIYCp5xxzNY5o3C8BmBCYjt+MIE+DvkQ1tRt3sspNlTglBMsmigHTjkHT4FrpFmA+DgAAIiaAZk0AMyUpRDlHN8k+sJvJPqsr9LfN8mWbgvQGOOrbE45RwPFM5kQ5fAs/vK0nJB/8OJ246vSLlGOEUklRTqSPG8KPogsN6CJvowU25hOOTJ+SQq6lChnUZexT3zV+mHZZr5CsGxZbvPqflU+Q5SjxVdZop8WR0RX1O8moBFf5euUw8eYdMo5qvtmw83IcnOZW/YhW1buM+yU4xDlsFuUFJhsruwRXFwWm1gpX9f9YNPTKSetop02LfFVXK5sUe4T9wdnfBX3UZ/4Kimi8HHKEUI3U/zD55nLe9Xq0vpz2Z+6YrIaTjkYVIgGOfjsE4cGAAAANPC4fgJu5jqgrznlzGzfQDjEdvxgAn04fSKcB60/MoGXjUKIcuCUExbbiq+yrR+ED66R5oF0oQcAABAdGMECO+NiFfjFwqKaeD++WU7yLk+IHv+s9u+kyxanHMuEfajYIpaUU84YUc6VfoNORHSrirCaXJTTwynHFHEkLU45tvgq9RSPbYCG46s4sqda98ktfR3SCYfXKeOykrSfKIfI3yWHqBScbNZEV5VTjoyvSqQ9cyFijqSg5aguty9aVJdPfBW3CzvlCFFONjC+SopZsmVZZ8opZ22PQWOnnCKvj3eOr3KJcmyDUfm6Ps9oAyJ94quqsprCMxWnV613c1XvKx8Pzviqtu0Kx5s+A5La9wzxD9fB6iEvXP1xtB2RY3CPy4P4quiYw+AzAACA/SJFzfg96c9sJ+kRXwV2QGzOM7GJiILCiK+a3Cknsr5kBU45waI55TjGrsesF7+zkYFrpHmA33QAAIgZiHLAzvj4Sw/2XYR2skU5Cc/iCR84AshG9E451XtDxTPZUVk3pijn5pZEOb3iq0ynHMekvhTJ8LLaci3xVaZTzrHhlJMkeoRRa3yVpyjHdE9poyu+SostYkGLcJHhbQ1xysnXfseHcsqp6kkT5SxFtJp0KuoQ5ZiOStlR7ZRTGE453I+WIr6qEE455vJE9T6tzon++V+qRU/8fXbdscVXtYldOJZLOeWY8VVVe/Dn66vSTYi/uzhpxlfxvrQ65cg+3+OJdM0pxxD/cH1y30uEKMcUvLXRcMrBDWk8YBABAADAWOR1CSYWejNXUQ6iGcAuiG1Ceq7H+17Ykign5raZxClnBvUQIhDlAIl2fKHtogXnSwAAiBqIcsDW+Utf/1YiIvqq73zPnkviweK0nygnaxHl5Hl8TjnHEzrlpItKuGA4otx6Ztx6ndsTdd0VX5UYohxX5FBhtKHpyNEWX2W63LALkRLdJKXbCsOCDxZ5JGndt1pFOWLbfZ1y8jXR1Xn5b+mUQ4k9vkoKUHhbZuRXG7moNy+nHCO+SsZqLY7t8VWU2AcG1D4Y4q2GU44lvoqdXYq8btd81VyeqC7fh36E6Cf+O6KP/lT9Wb4WohwZX+UjyqkEK7krvsqoq81lHVtFVL5eG6Kc3HBzcm2Xl7X1967vFcI1KjHOKQ1RjhFfpW2nzSln5FN5YPeoYxRtBgAAYCAqVRNOOYOYrXMGngIHOyC2CbHYyhsSHLc7Nr7qF/4h0Qf+mW0D9XaiZUqnnJjrIUC2FV+Fc0qcFLhGmgWzvYYHAIDDAKIcsHXymC70lif9nXJcNza2CftQ4QEGKcrhCfSjofFVR2XdFLk+tx5CfJXTKccQmOQdTjm2OB+eGDAjexpOOWZ8VRWHxMsnWQ9RTrX9rK8oZ1WLcmS/N+OrlKBFxldV2+oVXyVyb33i3UxRDkmnnKPaKcdW/65tJ5nutiOdcnLDvYe3L51yeH83q+byqiwJ0ac+UP776n79mRTlyLY33aRssGCFHXacTjlXdfmkcGdhEeX4OOVMEV9lin84Dky5CMn4KotYw7U9VR445UQHBhEAAACMBk4545jpb3FMYw8gXmJzicAE+ggmiq/6l3+d6P/9Xy2rn0HbTOKUs9H/xkieE/2tryP68I/tuyQ1W3fKibjfHiKyvdB28YLxNAAAiBqIcsDW+Z5//Wvq9WoT+AVDX6ecdOkWJZjRRyFji69STjlD46uWQpQj46sqp5xtxVdJkY0LKZZK0vq75k1qkTdddYjEQIEtzichTchiOuWoZRN9e/lad1dKkqbbjgv+fNEjvipblOtX8VVSlCMcaaTAKBvplMPL5utm3dpoxFelpAbDFsd1H7LFhzW2LdqDt5sudbGKKRRSopyzeh3cZizKsfW1NKv3VYuvWteClEZ8VYfQhfeLBUqm4I8FU1y+9aXeH2yiHB+nHBlDVT+S3l5WojqqSkZ+qfqshEmrShAm46tkf+oU/3B8lRkrB8JnDk+EAgAA2CuFuA7H70l/5jqhpk04oV+AbRHZhNhcj/eYkLHg5vvli50WZ1qmdMqJuI+uL4h+5T1Ev/6+fZekRv4OTirKgfNtnCDicxbgNx0AAKImEsUAiJnTo3rC9xc/cWePJfHgbd9A9Oav818+zXTHC4mPE0gomMIRogmccpZV3RguIE+9tVz37WeHrddFKoQWXZhCGxYL2OKrrE455iCcdGohXcjC5VJOOUKAsDFEOVLIlaR6bFPr/rDzS0+nnI1wyjm6Vtedj1MOi3LyHjcB6sZh06xbVxmJjPgqsa+2+KokcQx2CYFTwylHxldZHJdYSFMIgYkrvsosz0qIcoq8FviY8VU+bcyinOyoKVjhsiqnnCvdKUc6AsntEnU45QjBy1inHCWGysq+tHqobyN1CKqckWSGKA4TL/Ewi8FnAAAA+0Vel+D3pDcHMaCPfgG2RGxPqcfm7BMSKr5KvTFwPbn9gb45nIu5jvj1oHVEKsopivrhpxDbEvFVQALh8jzA8QcAAFETSbYOiJlX3DhRr1ebwC/6vvK/7rd8tnQ/bVBYom1C5fE3ED35FqIn3lS/p5xyxsRXXTUFB0+8kehPf3wL8VXV6awruoqodtYgqgQCjvgbs+zmcqYogF/LARcenDi5Xf1bOOFIt5R8o4s8Us/4Kvl5H6ecdFGun0Ujy7OyzfKVLlyRT3TJumXBRx+nHK4TFV/lK8php5xEF9QsbU45jokZ2VZpRrSp1pEuxCDKRhfZ8OuFjK+STjlru/BOlodFT0SVU84RESXGgEjRLXRJs3K5zZVdeKZEOdV6TVHO4qTFKactHk32+T5OOcJhx+bIszyrRTlafFWfAWMzvirw3xdQE9tEBgAAgPAoxHUJrgH6M9dJerk/c9s3EA6xCcznerzvBI6v4n+OEeVYxg5n0TZTOOVEOsn8wR8g+t4/SPSt/zbMtkR8FZBo10hou3ip2tGV3AAAACBo4JQDts6f+po3qtdnR5GIVHxhtxETdkqxuWiEyKOfQfQt7yW6/mT9Ht9UDxXPpMva2cMUlUwtyCHSxRqdyxpOOfxd84LWFI6op3+MmxdNUGHGV1XbMuOrklR3WcrXurtSkjaFPc79GeKUUwnKru6X/z66JuKpErGvJJxyLPFVfW4C5I17H6ec3OKUs5BOOdKpqCO+Ks30+Krs2HDKsYhyOG6pyOvBIo5m6+OUw+uX7jy8Xl/h1frC3sdZgMPno/Vl3UZEpRjIFOUUhoONDS2GaqBTjnQpYpYntWBJOyZkf+rYDpfHJaoD4RLigCUAAIDI6HFdAprMViCLaAawA2KbkI6tvEEy8remyO0PNM3hXFwIUc6hOeXc/QTR5V2iy3th7oMWXwWnnNlzcYfo5/+++3OtvXCNFC04/gAAIGogygFb5+lbp+r1p+9ftiwZIanDKcfmDBEbyinntH05F1qczg4Gy/vEV8l2SRIxqS8GSYqCaP1QdwqS7jHyL1lEIUqUU33n2CLKscZXSVFOz/iqXk45lRPP1XlZZ4vjWtiRpGKfZHyVEKAMccpR0U/GvjrLaMRXkXTKOSI6fbR8T4q8ukQ5SVILTbKlLlbJjTJZRTnslHNVvra1jWrDrOmUo0Q5Mr6q8Gjjqj3WF7oDjllW7jMcc8Vkx834KnWeahEPan3e4gzlQjnl5Pbz4eKkFiwlFqccrQyuWAozvgo3pPGADHoAAAAjKdi9APFVg5jrgD6ccsAuiE1gngcoFogFFV8lxkeGrsfqlDOH+yKP8YwuYu2j2oNnLLAKqC21+KptOOUEtK+A6P3/lOh7vpno3vOOBXCNNAvmeg0PAAAHAkQ5YCfw/evv+5v/ar8FmRoZMSSxOUPEBgsLlgNdbViUs77cTT30ia9qOOWIqB2Go3WkKMl01LE5hySVhX5hCBFMpxxK9L7DYgrej2RAfFUfpxyOXrs6r0UtLGhKUj0TnPdX1u0Qpxx2meFIo06nnKruNg6nnJtPE33TjxK96evq73SKcsQ60kVZZ/mqFo9I4Qjvr4yvksIi01lHluHWq4lOH6mFJ3leO+tkC/0ppSKnTuGacsq56nDKqRx4NpdGfNWxJb6q6l9t4kHN8cYiQusqr+aUI+OrTkuBkVxfkult1/Xkuzr+eL0YVIgGDCIAAAAYjZgoxcRCf2b7WwynHLADYjt+5uDGslemiq9qGaeIuW0KmtApJ7LztukGLd8LgW3FV6n1R9Zec0eNB17ZPw+pb4Lh4DcdAACiJmLFAIiJn/0z7yQiot/8pic7loyMbGmPr5qTU87RWftyLqSTyi5EOSqSyCMyTC6TpMJlRNykKlGOdMoxY3JstvlmfFWLU44UZrBgIpWf9xTlLPrEVy3K9V/eJzq6Xr7HYo+Enzomw/Unq9/nbfVyyuFBiioKqev4UE45V3W5uK5ZgPSqLzQcghwTM5oop9pudlR/l51v0jannI3hlLNxiHISoqfeVh477JQjRVqN+Cofp5yqXOuHDlEOO1OxU86VLsq5/RqiT39QHwg0I9ZspEKwxtXqExMhv2dz5LGJ3ZKUymPH1n4tTjmIr4qPEJ8iBAAAEBdwyhnJTAf04ZQDdkFsE2IhigWiQd0Ej1xNbhdFqLYZt/r9IsczBu7ILJxyAjzONFEO4qtmjxxztX5uWRbEB44/AACIGohywE549NoRJQnR57zy5r6LMi0sbDCxOUPEhnLKGSjKMYUv2yYVQovOZUV5kqyObrq4U7+/qsQUQ+OrckOUc3St3BaXMyFdmMGiHBV9lAgXmi4XFRaq9ImvWpT2tVf3iY5NUY5wyjEFRqqe2Smnx02Acpmp3FN846ukOKkrqsvllMN1qe3Dst6PzWXT+cYaX1WtZ7Mq98EmLPribyH6ot9fukyxKEftQ9aMr/Kxe1ZOOZf2iDZuO97O+kqvo2e/lOjiZaJPvb9+z0c8aIuv8nLKsTjsaPFVUpQj4qt4W9okimN7qjiIr4oODCIAAAAYDZxyRjHX32JtfwLsF7/ynvrhDxAvsR03cz3ed0FB08VX2SbK59A28iGj0U45kdVDTKIc2wOlY9cb0r6C+vhzOZrL9sK1c7zEJgwGAACgAVEO2AlJktAyTelqM7OLPpcoR8XCeLi2hMrp7fLv9VcM+74UiPg4a4xFRTgNia9KiE4fJXr4Uv1+W3yVmZ8sBRUqvspwIUmSMsJKChBkpjMLdFR81QCnnL6iHDO+Sn1fDDoVhb6f5raGOOXklVCjq1+YTjlcBq2sBknq75STLmrHn/UVqXgp5uYriY5vER3fqMvNN7fK2chyjH/Ff070+q8snXJUfJU4J6QLfUCkyLt1LlKUY9t3FupIu1oZZ/baLy3/fvSnxHY3pLW1dbvChcYW1+ZCc8qx9OPlidxI9R1TXNO1HeP4w6BCRKCtAAAAjAROOeOY7YRawE455y8Q/a2vI/qlf7zvkoCxxHb8xFbe4JhgPKvLKSfq37EpnHIi7aNyjCtEtx/NKafH2J33emPutzOk81yPiM950CG+AgAAEDQQ5YCdscwSWm8CujmZAqcox+IMERtPvJHoP/0pome/ZNj3pThmF6IcFkd4xVcZohwiorNHiR4IUQ47nLBgRS5r3ugkFqcc5ZYkPju+2RJfdaGXLc0GxFf1FeWsSqccJcoRTjnkcMphkQZvq8+AgxqkqAYsOuOrqs9VfJVFFGTicsqRIikp4FKxT5VTjqzrt34D0R//udrVpdjU7cpim7Z9OLpGdMWiHBHh1Iivyns45Vw44qs4hqslvurWq3VRTp82yDc02Ckn3xjuS2Q45aT6X82Vh6rvtURaIb4qLhArAQAAYBIKXUQO+jHXp2yLgCec+H6P7yNAvMQmcsEE+giMe+DBTjC5fQI1tr5ko5jg91i6KseEfGAvxLaU7TFpfNVMryF8uLxP9OPvLh/sC42uPqiNxRxg282FEM81AAAAvIEoB+yM86sN/eSHX6A7Dye8Edg32dJuAaoEGZEfYk99znBBjSbK2WV81QCnHKLSKedBT6ccq0ghqZcxhQgnQpRDiS7o4hs65awzxCnnuH05SbYUTjmVEwy7rSRkOOWwoCRtbqvPoEnDKadjv5RTDtdDIgRIjn1NiOwCDhnBJYQ9vB/ry0qkIuOrMqLTR3TRh4qHqgbV2yK4ltfqGDRNlGOcN6Tdswsug1OUw+KiVb0/pkjr2S8l+uh7xQCKR4RYIvqzcspp/0q5jHCvsW1HOuWY8VUqaqxrI1weiHKiAgNBAAAApoCdclziXdDObCfUAhb/YhJlPqjjJ7A+5gJ9bzhScFK+MXA9XaKcSPqSlSmccgJ0mfEB8VXTrTMWPvpeovf8ZaLnfn7fJWkix1zbPieK/Jxz4Bzy8QcAADMgcsUAiI1/+9w9+ty/8MNUzOXiL13YL3bVBHzETjljSXcsyuGJeZ8IJym8kE451vgq4ZRjxuu0xVexO4jk+JbuCqLFV3EckhDlSCFJG11CFRvcdzWnnKN6fVKAJAU0qemU4ynKKQpSAzTsONMlCMmMSCYpcnIJkJxOOTKCS8ZXscPMVSm4sTkt8fK5GEjj/tHmzHR0JpxyOL4qqwRRRnxVlwJFOeVc2fs411XucMohKiOszj9N9MKHqmU9nHKk440ZF9WGFl9l2Y4UuzW25Xlj2Tj+ZvK7MnsCfoIdAABARPBEaYKJhSHMYiLYQsji37nW+UESmagNfW88Y92fpZuy/kH9eazIh4zGOuXEVg9WUU5Ax9m246tC2tddEXJfVe3iamuMxcyC2QrrAQDgMIAoB+yF1WYmF38cAWSinHIOWJQjBQE7ccoZGF+VClGO5pRTOZyY4gEp+miNr8qb7X96uy5fkhjxVaYoR7r5eAo2fARJTJqVT8pcnQtRDpfNEl+lxETV+4vK6cT3xl4bDFiX/+6MTmKnnFVdrq6ors74KuG2ky0Np5y1vUzKLUbkhJtxYzaWZ7U9vRLlDI2vSurt2pxyUkPAZBPlPPul5V+OsLL10cZ6hbhG1atPfBUPylXfM4/LrvgqbXDHNdlmxlfN5Hdl7mhPZ2EQAQAAwEDglDOO2T5lG/CE02zr/ACJrS1jK29QVL81o+OrCt0pWb0/h2ixQ3bKEZPjIR5n2jgcnHImIeR974yvglPOLOgUXwEAAAgZj9lrAKbncr2ho8UMNGEcAWQiJ+APlcziRrNNUiG06EJzthHxVQ9fImVPbIuv4uWVEMUWX1W9bxNafNm31sKfJNXtYzeW+Cpbedv2p5dTjoivOr5evqdEHAnp8VVSlMOORNW2fG9EpXhn4xvLVW1LCVikoKavKEdEyrGII13Wdba5sotHiGohT76p16NEOW1OOdfK+iXSzwnpUndJ0gaxHCinnEvdhYrh+uD1bq6a/eHR15VxXL/+vqpMm/q46dquFl/lIcoxnXL6xFepG8uO7RTG8RfioAhooj3BjoEgAAAAQ5FOOfsuS4SEPKk0hpCvM2SELIib2IQUeKp+OFuPr5pB20zqlBPJMcXIyfEQ2xLxVdMTsoCsM74qYDdB4M8hO1UBAMAMOGDFANgnl+ucbuy7EFOQLuw3NoivMkQTI61+fUiE0KIL2S4yvipfE13eJTq5VYspjq7p39WccmzxVdIpxxA8PPP5ckX6gGzDKWeAKKeXU86i3Ob6IdGRIcppc8ox46sGOeWw802XS0vljJNbnHKc++pwVZFRYDLqjNfDTjmuuk6yaiDNEOF1OeVcnetPxaWLUjjG/YvL1umGVG1nfeGIr2JXoasqZmvdXC5JiJ56K9Fzv1Btd9MtHLTFV3k55YjvFRbxj3TK4fVpjjdmG7qccgwBGQgfbfAHbQYAAGAgcMoZSYCTh5MApxywA2Jry9jKGyJTxFdZRTlzmFydwikn0j6qxVeZD+8FgOlYPd2Kjb8HRJfwZZ/0cco5xLabDXO9hgcAgMNgBlYlIBa+7WveqF5frmdy4ZAu22+sEV9Vssv4Ki+nHJso57HyLzvZOJ1yMosoR8ZXJbW7TJtgw6wTdoOR8VauZRvr4kipHk452ZLo6l75moVHcttS6CCdTpQohp1yOm5Ef/QvEP30d+nLKVcgH8eVRT14IMvl2teu+Ko0q9slWwinnMvKOcYhUklWJ+2OAAAgAElEQVQz0uKr1PZa2vjorPzO5koX6jXiq3o65dj6OB9v+arcF/me5Km3ET3/S6WjTr7uEV81xinHsp1lR3yV3I5re/zUovk9EDh4OgsAAMAEKPcCV8wlaGWuk/TaU+D7K4aVudb5IRKbkAJ9bwSmO+vQNi/sYydzaJtJnXIiqwdNlBNgW25LlBOyK922Cbmvcls4BUMH3G5zImRhGAAAgE4gygE74+s/7xn1+mI1kwuHNLPn8qoJ+AM+xKRjzU5EOSy0GOiUc/po+fehKco507+riT4sTjlUPa2bb9rFC2adcByScvyxlLFrXb2ccsT6bU45ZmyRGV/l45RTFEQ/+78TfeTH7ba5Pk5SqRGDZoqCTLpEOUlat4uMr2KnHJcoh+OrGk45LU4zy0rsdHUunHKyZuwdP+ndhhLlXNj7OB9vm1Ut+LGKct5aruPFD1cipI420AQvQ5xycvt2NFGOIb7xvrE0nXICHBQBTZBjDgAAYBLglDOKECcPp0C7tgisX4QceQH6EWJMTRtzPd53xViXHCKyuu7y++WL8dvYGwWNflAm1vNjTKKcbcRXma8PgZDP/12CoUNutzkRch8EAADQyQErBsCuefrWKb32sVLgMBtRjjm5zvCF8EE75exYlNMrvkoKParvnVWiHOWUc16uyxRAJKm4ALaIFLT4qjanHPGddEm0ZqccwzXEfG1dV/V5H6ccWQdKlLMU63PEV/VxyrnzcaKLl8tjJLc55Xj0C030IlxRFg4BkmtixhVfxTFKq4ftIpUkqx2QnOUzYAei1YO6nji+SnPKsUSdmahop41dbJNmRJSUAy3cl2z94am3ln+f+4XuPkoknkAXGel9nXKKTXM7ixO5keqPEPJoEyquSDI45URJyJNlAAAA4kE55YBBhDh5OAkBPwU+2zo/QKJ1yomkvCHRuAce6gTjiq8K+Jzli/Z7fKBOOXkEopxJnXIOWNwRYjszqmyOcVqMxcwDiHIAACBqIMoBO+W3vPVpIiL6O//yY3suyUSkizIGxkS5YrRM2M8dTZSzgwFzFrN4xVdZBC+npijnYRk/ZPuuOWAgd0/FV23ahRayThbHdeSQGRNlvrauq8M9xoYUL7F4RNWddB+hSrzB7jIsiqlEFW2uJs/9Qr2MullIhCjHxynHcAzycsoRN5c/+ueJ/t7v1kU5Mr6KHVvWF2WbuUQ5adXuDaectvgqdsp5oJ8TzPgq8phUkn3A1seTpBb7tDnlPP6G8v3nfr4SIfkIo7KqnW3OUK7yCjFPboly84mv6nTk4SfkR+bXgx2D+CoAAABTIJxyYp3I3CdzHdAPecIp5Ik80I/Y3E1iFTwEgeFqOzieqcspZ8S6984E8VVwytkO3B7ZEUQ5U6H6aoDHa1esUcgRn8CfLvEVAACAoIEoB+yUvLoA/IFffG7PJZmIdGG/sVHxVYfslCMEATsR5VQCKC9RTtIUApwZ8VVX583oKqJKnNESXyWdclrb3xDlrC/1/ZCCFV/Bhss9xobmlMOiHBFf5XTKMeKr5A34v/1+ok++r/63EuWs6+Vkv+gdXyXazdXOSaKX6blfIPrU+0thCO+bdFVaSqectVsopOKrNqS1Xds+cP9ZnYtzwqLcrhTzSdGTC9nHXG5QPNDCAi+bKCdbEj355rJeNld+wqgk051yfOKriCoxz9oudrLGV/FAnhAAtQGnnDiZxcAzAACAvVMU1SUJ4qsG0XD+nAsBu07ArWQ+xCZqC1EsEBP8wMkYOA46zy3vW17HhIw7H+MkJP/Ggix3iMIiOQ43aXxVwL+12ybkvtop1ghYuAx6ENk1CAAAAA2IcsBO+bxXP0JERC+dX3UsGQnpgii33NgoV44DFuVoYorA4qvk8iwIOLldlvPBi+W/Vw914YD6XmoZUNWscqgcbOlyypHOJ1KUsyunHNE3j6v4Kq47U+ggRRVqW5XgQz6B8YN/mui9/2P971//+WoZhyinb3yVLJcrqku2DxHR5b0yzkmLrxLCHnb8WV+U5XS5W7EwJV/r/aI1vqoS5VydC6ecrHa0+aV/TPShH9FFTy6kaMcmtuGybK5EfJVjuafeSvSxnyF6/z+p46xaty2EZmZZ2liclAM/uS2+Sh5bpijHuLF0RZKppxbZ1Qk3pFGgDeChzQAAAAwFTjmjCHlSaQwxOOW0OY2COIjt+IEgbDhTxlcRNSfLtT4Ua/tM6ZQT2flRCvRCFLuqcbglnHKmIuTzf1fZDrnd5kTIfRAAAEAnEOWAHRPQzckUZEv70wbKFeOAD7G+4ouxqEgiT1GOKTJJ01KYo+KrHhAtrzW/p4lyLCIFFV9VtIuyZJ0sjjriq7bglCPr6ei6/l6SCKMcwynHrGd5E7BZEV3dr/8tnXL4mMh6irWU6MUQbjgFSMbEzMXdKpoqJzVxI/dBOeVcVHFODpENRzgVm1rIQ9TexsuW+KrVQ6Lv+2OliEl7ssy1W2I7rj7OTz+1xVcRET31NqL1Q6JnPp/ot/3P7dslqvZd3uz5inKOhdjJdMqRdWj0LV/xT8MpZ2a/L3MFAwcAAACmgK8D4JQzjNkO6Af89H5s7iqghQAn39tA3xuBEV81eDVVG5iivDlMkhc0fsyvCDgSqI3g46tYlHNsf6B07HrN14dAyHGASvzrEuUEfI0E/IHQFgAAoqblEX8ApufpWxbnkZhJF6QsaKUAhyfgD9kpRwoHdiLK6RFfRWQXv5w9WsdXrR64nXLUQIorvqoob9RanXKk84kQmKRZ8/OtOOXY4qtYlNMWX5XW32f3GKbYEF1WopwHLxHd+Vj5egqnHLVsVS6XAMnmlLO5cuzDstznJCtFKrklZkmtN6vbfXlK9NAon40jEV/F5U0X5Tav7pX/5rJ1DfRpzkouUc7SEOU4+sNn/zaiu58gesefIDq+0b5domZ8VR+nnNVFdSy0OOWY8VV53ry5tN5s8hPyiK+KFgwiAAAAGAyccsYx00l6dV1dUHBirRAnbMEwQmvLn/4uol/9SaLf+Xftn4dW3ihhd9ahTjksylnb3x+z7r0zpVNOZH1UOiCFeJxxWRZHeoT6VOs1Xx8CIbYz0xVfNQtnLuAUeQIAAIiCA7bxAPvgba+6pV6/9r/4fnruzsUeSzMBPCFvPnFwWU24H1mcVg6FXYtypNDCB247KcI4e0yPr2JRhbmdxk2YJb6qyNvFC6ZTjipXpv81l7Wui4UqQ0U57JTD5UjqdZpOOSombFG7xzD5uhabPP+L5d/jm7XDjLYNcgtgbOU0BTUuwYk1vurCLsrhPro8Ldu7aBPlpLWbkXTKaXPDWnJ8lemUI/qoKRhyoYlyHIKkbFmeizYd8VU3XkH0Vd9OdHq7fZtq20lVp7a4thaUU05uccqRohyjXRrHlWN7yiln5AAp2C2HPIAHAABgOuCUM46QJ5XGUEwwObwt5lrnh0hobfn8LxF98t+4Pw+tvDFh3nMOXo9rsnwG0b6a8+9Q0VLsohzplBPQb498OG7K+CqtnQPa310QsoCsU6wxg/MNwG86AABEDkQ5YKckxo3s9/3cJ/dUkolQohzj5ubTHyj/Pv6G3ZYnJDRxzAR2v53b6xtfZQgBiIhOHyV68Bvl66sHtahC+56ILLM5h7CAobAIETTEd2xxSKb7ThumyMQHm1OOJoARTjm5p1NOvq6dcp6rRDlPf67hlCPFWn1EOYn+17WvUpST50SXdyvhi3AuUsKnah2Lk1KUk69b4qvSWuwiBSWtTjlVva4e1DfFSaaLajZXpD1Z5sLHKSddlutbV+Ihl3inLyq6q69TzmkliLK4RrlcqIh63lgm4wcAwW4pDngADwAAwISISJGQJsBiQV0vz+0p20LcgwXWLzCJMh9UGwbSx4q8fcI9tPJGxVTxVY5z7iycK+CUg/iqAyLkOMCuPqi1W6znGxDkuQYAAIA3EOWAvTL2YZO9w5PjG+Pm5lMfILr5jL8TxRzpG1M0FuV845nK5xVfZRHlnNysnZCs8VWVhX7eFV8lRRa2+KohopwBTjnZcd2Puc0SIxJIOrmkQpSTZnpWcb4huqpEOfefL0UiN54qBwh5kCXt6aDkcspxuQIl4mnp1bl4fdF0+1FOOWeVo8vaLRRKsvo415xyPEQ5V+di/7O6Do5vEq3ZKafjZCgFXi43qOzIL76qL0p81VeUw045m2Y9aUI0I75Kc+UhvU0lXG/KKQc3pFFwyAN4AAAApkNzL8DEQm/mOqAPpxywC0JryyJvF9iFVt4oGRtf5SHKibV9lHMd0eDf4+hFOUWgbj9Ve0weXzWH2LWBjD2fvvzx5hzCVHTGV+EBqXnAwrC5CesBAOAwgCgH7JV3f/8H9l2EcSinHONC6Pn3Ez352bsvT0j0iV+aAiW08HQHsYlfTh8R8VUP7G4exzeJLu6Ur13xVUrI0uIEI4UNUmCiRCh94qs6hCo2WJByfF28x6Kc1C++quGUsykFKESlQ83Jzcq9RTrl9I2v4mUM4YZPfJUST1HZnqZTjhLlVE45Re4W2SQOp5y2Nl6c1tuW8VWv/Dyi17yd6HVfUbn49HXKccVXLQxRTg/npK5tF7m4gfcV5ZyUrj22WLDW+Cp2FerYTsFPLUKUExcHPIAHAABgQsR1AH5P+hPyk96jmCBGZVtAGDEf1PETSB8r8vbJOfS94WiCE6LB5xUlylnb3+dtRckEYsggBS0eROWUM6UoZw79diBj+urVOdH/9EVEv/APpy0T0+WCOAcRIAjzXAMAAMAbiHLAzvmu3/X5+y7CdChRjlC5b9ZEL3yQ6Mk376dMoZAkuvPKtukbX2V1ynmsdPa4etDilHOL6JJFObb4qpSIqqdkfJ1ypJhmVHxVj7gi7rvs5kJUijrU+sSTTpooRzrlpPrNXr4unXLynOjibilgSjM9vmrR00HJ6ZTj2Nckrdvl4m79/vqiKcSS8VUsJnIJhdKsjoXydcpJ07IPXZ3ropzPeifRN/5gWT+blWWgz7Zf4nNXH8+OynORKudETjnsiMRtOMQpxxQvyTrkvsZ1rwmA2uAn5AN9GhrYwdNZAAAApgBOOeOQT/fPiaKorztDm6yYa50fIqG1ZZHrDra2z+Vf0INKADp2TMtLlBNp+xQTiCFjd8rJN2EeZ3yOypaIr5qKMef/1UOi9cPaoX1qOvsgHpCKHs2l6sCOPQAAmAkQ5YCd8zWf85T27zyP+EJQiXLEjfVL/650qjh0pxyiWvSwS6cc3/gqU+xBVMZXEZU3SKuHREeO+Col9miJryqKdicYTWQh3WMM8Yn52rquAU45SpRjccqRg06mU07a5pRTHQer89Kl5uRmudwk8VVjnXIeWtZRrXt5WsduudpMi6+yxI25UKIc3n/RP7Ml0eayKm+XKEc65ThEOemycsqpytlHpNW6bSO+qpdTDsdXtYhyrPFVcjuO7bFTTuN7IGgOeQAPAADAhMApZxQhTh5OQgzxVYgbiJ7Qjp+iaHfBCE1EFDNj46vM41/rQ7G2D5xygnfKWRxPG5l0yMKAMe3M5+lt1VlX2fCAVPzINmwT4wIAAAgWiHLAzslSfYL1/GpCC81dw5Pj8ubmU+8v/x66Uw5RXT8hOuXYxC/Xnij/3v31Ulhlc8rR4qssIgUWheSbjv3uiq/alVOOEOVoIioRCSTjhzSnnKwWm+Q5qZu6y/tlfNWxEOUo29yhohwzeqplX7ld2NGIqBLlONaxOCnLLLfXKEdmj6/qEuUcnRnxVWL57Khap098lfE9GxmLci7bl+tLwpFsFmeoNpYnRKsLu2tUmtbCHNMFyfvGsqjm4hBfFRd4OgsAAMAEwClnHH3jqz79y0TvforoN361e9k7v7a/iQLNgTKwfhHrpDNooq5hA+ljiK/aHj6utn4rKv+YsTLFDO6NJnXKiawO5G9paLF2RGIc7tgdaTRmvebrQ2CMq9OuRDnO+CrpJhhQPwX+HPKxBwAAMwGiHLB37l1ELMphEYO84P3U+8sb0ifeuJ8yhYSKr9rBqcZHrCGxxUTdqFycXvpI+dcZX3W3dsMx10HVxECRNyN7tO1L5xMZ6cRONIb7ThujnHJkfJUQUclBFS2+isuX6U45chDw6r6Ir1pUVr7V55orUIegRVumqoO3/UdEX/fX9PqRuJxy1heWfaj2d3lKdHVP/6yx3kQ45XjGVxERLa8146uYxVEVX5X7t3HbNrNlKfJZV+KhSeOrhjrlXNqdcvhzuT4tasAyQGAOGsApJ04wiAAAAGASpFPOvssSIz1FOS9/rIxcuPvJ9uXOXyT6q7+J6Jd/YFzxhiInh0ObcOorhALhEprIhR8Kcn6OvjcOGV814LyiORu0OOWEds7yZgqnnA4xQajIc0GIEVzy4bhtxVcd2kVYFE45LlFOjvGz2MF4GgAARA9EOWAvfO+3fIl6ff8yZlFONYmcG045j75Od9M4VLJ9xFf5OuVYRDk3nyn/vsiiHEsbntwsL3yv7pO6+ZQaBeUqYnEHIWM5RhN5WMrVGW1Ufe6KdLKR2UQ5LKIy46vEIIva1rISa1iy0a8qp5yTm9Uy0inHIkBqw4yveuwzib7g97qXd8ZXPWg6snD/9HHKSRxOOV370HDKkfFVR6VoRd4Yt+2X/J6N7Kg8F3E5fV2jumDxVV+nnMVxKYYqNvZ64npU8VWG4435fgN+Qn7kU3lgt8AyGQAAwBQUpF+zgn70nVTia9muCdPLO+X16IMXh5dtFBM4NmyL0IQcYASBiVzglLNFjAdThohOZL2bMWNziAGa1Cknsjo42PiqAxYGjBLlbLmfd5atEA/MBXaNBDyRvxmRiRgBAAAQEUQ5YE/8plffpq9929NERPTV3/keulxHeiHBk97yxvpTH0B0FbNLUQ6LHbIO5xLG5pRz7Yny/Rc/XP7b5ZRDVDrBqBsdM77KcJexbr/DKadvfFW6cLvH2OD6Or4hymGJryLSnVz4Bi5dVPvK8VXiGLg0nXLWtXhHtk+v+CrPCRcpyrm4W79vxleli3qdy9PSzUbuX6McQpTTyynnjOiqJb6q2JSfddWF+T3rMgsjvmpCpxzpXuN7PGtOOZZ6UqIcQywlBUBtwCknTmbxNCgAAID9I5xyMLHQn96inJXf8vmeJyULMeEUWrcIccIWDEO1ZSCdjMvhio2LNRooFMaKP7X7nxannOBOWr5M4ZQDUc5WkA/H+Y6z9Fmv+foQCFqU44jJk58ngV4jAT8O+dgDAICZAFEO2AtJktA3fulnqH//sb/7vj2WZgQ80cxPHGzWRC/9CtHjb9hfmUIi3aUoZ6BTjhQ7pFkZYcXxVUcWUc7xzfLvxR3qjK9qi2eSUUpmGfh9c9m2dfUVYLTFV5HplGOLr1roAhhNlHNPOOUs3E45PvFVfYVdiZiY0ZxyHgrBU6b3k+VpacffVqYktTvldO3D0XWi1Xm9/6ZTDlEVN9UjvsolPMuWlShnpa9/LElaRZANia96WA7+WOOruB4NwVdD7OZ6KrFyyhnz1CLYA/LJHrQZAACAgRTsmJfg92QIQ51yOkU5W45m6AROOWAHhNaWtnty2+ehlDcUfvmHiH7tZ/2WHRVf1eaUM4MHFuCUo4tyQvrtMcfhXOeILr7nDxL9/D+QK25u41DoiohqQ10jbamPdJ3r5Tj1obXbXJiDuxoAABw4EOWAvXHzpJ5Y/sFfem6PJRkBT+zzDeS9T5YX5rdfs78yhYQSBezAVl5FEnkKEawxUUR042kRX9XilHN5l2rnEItTzmbdEW3EQoSFLtSwlctHlLPoKcDgvitFOVJEJd1HchE/JF1N0qzu+/JJjPvPEVFRO+UUm/qGVXMF8nHKcbSTC2d81UO97DLaaSFFNh7xVba4MRdHZ6ULj8sph6iMeJoyvmp92d85qXXbmT7I5B1fdVLW2cbhBLQ80dfX1/Gm4ZQT0OAXcIP4KgAAAJMAp5xR8O+xtyjHc8J0324HcnI4tGtDCCPmQ2iT712TxOh7dn7kzxG996+5P7c9lDI6vip3fxZt+xSkPdQ1aBUzEuWEtA8yvopoeITVL/8g0cd/Rqz3gB+0GSMg27ZwWQk0XYIhcayG8vsF+jGL3wwAADhsIMoBe+P0SJ/MvlpHeDHBE+xs5/3yx8u/EOWUsKNHLPFVREQ3nya6ul++HhRfVT2tuzrXBS+N7RtRSm3l2qpTzvX6PRZ7aKILwylHRj8lWT14Im/67v56+ff4hnCTqgQtUgzTJ77KV9iliXLu1O9L4Uua6nW+FCIbl5BKxlctPUQ8at1mfJXNKefSo43F/nfGV11NF11FVNaXjK/ydsqpyrA6b3fKMeOr8g35DRCIJ+SJcEMaC4c8gAcAAGA6ikJocvB70pu+8Tu+T3fvPSYnAqccV8QQiIfQJt+7JmJDK28o5KvyARIXyhlZuLMOoc0ph2bwwMIkTjmR9tFYRDljnXLYQdtcr/n6EBgTX7grUU6bU46Kr4r0fHPoaL8nA9yaAAAA7B2IcsDeuHGsxwz97Z/+VXr+7sV+CjMUFhjwhfXLHyv/3n52P+UJDSXy2MGp5pFnic4eJ3rkM7qXJRIOLMbgyo1X1q+l+ILxia8q8tIdpVWU43LKWejla6zftq4BTjlZmygnbYmvSvUyKqcccXN/95Pl35Ob9X6sK0GLjI3yia9K+wq7RITB5b36hnP1oN6nZ76Q6HVfXn/FyyknrZ8q0pxyOkQ5R9fKbdtEOQvplNMVXyUjzlzxVUdClOMZ4+aDGV/VxymHqBQl2cROSyO+ynTKUQ46jid5lFMORDlxAbtdAAAAUwCnnFFsK75q324HHGvGr0MixAlbMIy+TlPbBk45wyhyd52ZTBVfZW5vFuKGCRzKVL0U4Z2725CCuBCPM65LfmBqsChn4+6rMbXXFIxp5233kc7fgkKMwx5Yu80GPOQGAACxA1EO2Bu3zvRJ43d//wfod333zziWDhTlAsJOOZUo5+Yz+ylPaKg4pB3EVz3yWqJv+wjRo56inDanHMYmqlHxVXeIrPFV1cTA1TnR8XXz22I5IwaKsYmFtuqUI/ZRczYSg04NUQ63ayYGIcTN/b1KlHN8y+KU0ze+isvUxylHiHKuPV6Xj7f3Bb+X6Bv+j/o7S484qiQtHW2IdLFWa0QZlW5Bl/fqc8Rgpxyf+KpluZ31ZT3oMgXSEal8w+97yinngb1eG/FV4okdr5vLQu+XGFSIA20ACm0GAABgICy+SBIMSg+h78QQX8t2LW+Ltt0l8r4ltOuMECdswTBUW+63GDVVQTqdcoIpcBjkm45zVU+nWOdqWpxy5iBuKGgCpxzRDjHVgxTohXicKaecagxvaHyV3D8iYx8D2t9dMEZ8vO/4Ks0pB9ciUTILIScAABw2EOWAvfJ9f+Qd2r8/8unzPZVkIKnhlHPnY0TXn9In+Q8ZvvHbhVNOX5T4xRALdDnlnEinnJb4qqv7ugtNA3bKyeyuOH3iq05v1+ITX84eKwUit19dv6fEHtJ9xBZfVZU3TT2ccliUUwlatPgqH6cch3jKRZLU7XJxt3RPUp851rHwEOWkQpjiszxz+ggRFUQPXmqWIevjlOMpyslX5UDLpPFVlfiqr1MOHz+rB3Z3HxVfZTrl8ACCsR1zcEs+CS1jy0DYaPFVaDMAAABDYaccfg360dPpQ00kdYht8j075Uzh2LAtQnNXAcMJrS07J2IDK28odDnl2OKrhpxX5HfMNprFBKs87w5dhRTlRFQP0cRXjXXKyfXoxVn024GMOZ/uLL7KdV4TTjmhXSMBP7TxNMRXAQBAjHTkbgCwXd76qlv0R3/z6+mv/fiH912UYfDFrIyvuv2a/ZUnNLIdOuX0xSX2uClFOWfN7y1OSlHExV0xSGMIaKaIr9LW2VF/7/pL/Z94ufEU0Z/4ANG1J8S2hYhKRgkVuV5fqoxCqCJvKlmUcyxFOVX5hjrl+D6dJsUZl/fK/VTrcgholj7xVeK7Psszp4+Uf88/XS4r21I65XTtnybKcWwzXZaORJvL7cRXkaW/tyHdeqzxVSfUELQRWQYoqvfvP1f2I+WGJSfjksMbDIqVOTwNCgAAYP8ocS6ccgbRd1LJV2wTRHwVnHLAlgmtLX3jq/iY+NCPlveLMtL5EOl0yrExMr6qTZQT2jnLlynOuzlEOVtBiXKqsad8KqecAxbljBEf70yU41i/dMqJ9Xxz6OAhNwAAiJ4A7SvAofEnvvqN+y7CcEwL0Jc/rjuPHDp84xeiU44zvqpDlJMkpdhEOuVoopmkdD7J1x2iHHbEyQxRjsXBp6v+zh4luvGK9mVsXH/SEIlIEZUZXyUihri8aWZ3yrm8W/49uVnvz9rilNPlMkNkFym1wfFhXA4pOnKtwyeOSn5Xik28nHKI6MELzXVrTjkd+ye343TKOSrbYSvxVTn1nvDSHIUs+7c80/dbPbGTk3WA4If+DNH3/IH63wUZTjkYVIiDA7a6BgAAMCGVOFde+wF/+k4e+k4kBeGUE+hT4CFO2IJhmCKXfcN9vTO+qvr7E99B9JPfuf1yhU6x6TgeRXzVmAfN2pwNZjHBOoFDGZxytkMjvmqEUw5EOSVj2nnrohz+LXCJcqjlYTgQBYd87AEAwEwIcKYcHDqbPJCBDR9YMJCvywGQO78GpxxJX0HFLnE55dx4un69cMSQndyqhSflSsTLpHRoISI6uuHevsspxyYW2lX9SRGVK77K5ZRjs8E9viGccq6qbcj4qh5OOb5jYKZTzjWf+CoP5xtNFHNcF8jbKefF5rJcF/mquy584qvYQeaj753WKYdjyqwitBa6nHKOrtudk3gwWQluqs8vXq6PLSLSnHIQXxUPGEQAAAAwBXDKGUdvUc7Kb/ltTzh1EbJTzt4FS2A6AouD8nXKkTFXoZR9nxS5n1NOov437PdGc8oxxk00UU5g5yxfJnHKkfeIEUWyyGMrxHM8l2UxMr7KPPafykYAACAASURBVGccsvvtKFEOf3dLdebzW4D4qrhpc14DAAAQBQHOlIND57d+10/uuwj+SFHOvefKwcpbcMpRxOiUc3RWim6WZ3Z3D6LSAaYtvooHInycctJUF3zYxEK7qr+TW0RnjxPdfpaaTjlVGV75eUSv+eK6rHxTad7cJ2kpumiIcvrGVznayQWLM/IN0dV9opPb3eKwpXR08XDKSRfCVajDKefkdvmX46skmmilR3yVSwj02b+1FAE9fKnODJ8CJXgRTwr6oImdLPX0Rd9E9Dv+L307RG5Xns3KeIKuEMIdxFdFwxwGngEAAAQAnHJG0Tu+ylNss+/4KirCnXAK0UUBDCO0tiwc9+Tm53LCNpSy75N80y4AsZ5DthhfFdo5yxtxXw6nnLDKr5xyWJQzIL6qKEiNC9ZvNrdxKIy5ztm2cFk5mbvOa8JNENfOkXLAxx4AAMyEAGfKwSHyh/+Dz1Svf/ETd1uWDAwpyrnz8fL17Wf3V57QUMKFAE81PFhrE97ceKUeaWTSFV/FtIlylMNHZnfKkSKGXdXf0RnRt32E6A1frQsk8k3977d/C9Fv/xt1uZRTTl6/R1S65CRJuyinT3yVrxCEBSTsqHJysx6AcAlfusQjvF61TFrtR+IWbjEyvspcdx+BkhYz5nDKWZ4Sff7vKV9PHl+1ESI0X1GOEDvZxEs3nyZ6/Tuby6gbS2M7mytjcMF0ysGgQhTAKQcAAMAUwClnHGry0LPulANAx/Lbfgq8CynaDu06I8QJWzCMvsfPtlGiHFdkiemUMyCaeI4Umw6nganiq9qcchxCh5iQ593BTjmbeuwnpnOkVZQTUDs24quGinLIfR8fU3tNQdDxVR1lg1NO/MxCyAkAAIdNgDPl4BD5U+96k/bv88s1rTcRXNjLG5uXP1a+RnxVjYrQGTGAsS1kHJPJzaeJli2CGhVfZREpyPUdXe/evhZfJUQemlPOHuqvEV9lEVWkmXgSo7q5PLlV/j2+VS9DRLQeGl/VV9hVTcywKOf4BtGiw7FJc8rxiK/iNuuKriIiOq2ccorcHV/F5W5D1r9LlENE9IW/v1zXpPFV3M59nXKEMMinrqQQTP+g/LO+tDjlyO/ihjQOCsdrAAAAoA9wyhlF7/iqvk45+7LUDzi+CqKc+RBaW/aNr5ICgkMmz/3OVSwAJRo2CdomYJiFuEE8LDPGKYfHfmKqh1icclR81YDfZtv5RXO/DWh/d8EY8dXORDmu34KiZdwNRMEsfjMAAOCwgSgHBMM73/ykev2WP/dD9K3/4Of2WBpPpFPOyx8tX9961f7KExohx1e1RRq95euJ3vLb3N89Yacci0hBCmi84quEU47mjuMQ+uwMR3yVtkha3wSYopyTm+Vf5ZRzWf7V3GF6OOX4CpNMp5zjG7Vji2t70inHtYx8P8nK/fBx+lkc1wKvhihHxld1OeWIz9sEN488S/SOP070hq/pLpsvXKdjnHK6HIV4O0TuG8vNynj6UzrlIL4qGhBfBQAAYAqkUw7oz1BRTteE3rYnnLrQJpwCu84IccIWDEO1YSB9TN2T+4pyEF9FRN1OOVOdQ7qcckI9Z/mi/R6PcMrhcY6Y6kG6yPSNhdwFXCYehxsUX2X57ZqDw9NQ8hG/5fmI6CsffH4LfMZhQbho42n7EsADAAAYg8ej6wDshidunGj//ifv+yT91d/xeXsqjSdafNWvEZ09XkYAgZLeLic7hAUVtrJxBJCLk9tEF3frGx5XfNVxm1NOUpdDRWnJGKvU/npXaE45hUOUk9U3AQ2nHFOUUznlpH2dclrayVruKsbo8m5dji5x2BCnnGzp5/5CVLrlrM4topw+8VXCWalLFPPOP+9XLl+SrOrrLMrxbItlR3yVSWqIcsz93FxZnHKUVU5Yg1/ADUQ5AAAAJkE45eD3ZAA9Jw858qJr+W1POHWh3bcE1i8gypkPoU2+mw/KuD7ncucQ5RCRh2OQLb5qyHlFfMecLOdJ8qjdi0b+HhdFuY6Y46vkMRXSNYnplDMovsoSX3nIETpjfsuLHYlynOsvxLjbgbXbXIBTDgAARE+AM+XgUEljfMiRn+TI10QPXyY6e3S/5QkNrp99xC91kfQUe0iOb5YiC37KxCWg8XHKSTJRFsONxbbOnSHarMjtbSjjq/jm8qSKazKdcmzxVT5OM22ORtZim045N+sBCNc6pFOOq0zyu+xu5FN+IqLTR+zr1qK8Oo4R/m5bdNW2SNNqkKlvfJUUO3nUlXTKkQMEXDebK2MQUzjlpIthA0xgDyC+CgAAwARM8WT+IdPbKcdzImnbE06dFPV9VGgTTqEJOcBwQhOZq77V5ZQjXT3QDynvcMqxMTa+qiGcEmKUWH/Lxv4ecxtkscdX7fv3zwKXJRVj10PX4RLihLS/u2BMO+8svsqxfumUc2jtNhcgygEAgOiBKAeAMSgXkBXR6iHR8rR9+UMji9Qppwt2g7m4U73hiq9qccoh6ZRji6/at1OOFEhsWpxyDHvU00qUc3yj/Gs65fRxh5Hf9xWCJERE0innercoZ+kjysn0ZbIjf9tXJcoxnHIWMr6qY/+UU05LdNW2UPFVDgcbF9r+9RDlqIHRHk45y1Oi9aVfucB+OeSn6gAAAEwMP5m/73JESF+BiO9E0phYhylwOXyGAJxy5kNo17PqnrxlIlb7C6ccIqrqwSO+amxUotZfLE45aeyT5GOdcqo6SWMW5RRhnuP5NzETLu+919ERXxXS/u6CMe28dVGOcENzfa7GPAP47QIDaHFeAwAAEAWBjlYAEAnqaYMN0eoB0bLFGeUQUbFBITvlDMjTZReYhy9X63CJctqccoTDh80NRnu9h/rT4qtyu1iFHVSIRHwVi3LYKaf63qYSTAwV5fR1ylldlP9enhJlfUQ5rvgq2R5Z//gqIotTzoD4qmwfohyOKdu2U45rMLLa3vrS7ZSzOCFaP/QrF9gvh/xUHQAAgOmAU844ejvl+Ipytjzh1IkQ5YQglpCEOGELhhHahLS3U44Q74RQ7n3C4xytk5oTxVcVLZOocpI8tHOWL5M55UQcXyUfYgqp/EVe/iby2PWg+KouUU6k/XYoQYtyhPDS9XmoboLAD4ynAQBA9ECUA4Lhaz7nqX0XoT9885zDKcdKX0HFLmGRxRROOdo6kvrv8sy9Dh7USaRTjhB5uFxzdk5R38ibKLEGCVFOVTeTxVexeMrXKacS5XC0WLqsxSGuesyW9Y2pS2ijxVctKlGOp6CLhUrmujWBjadTzl7iqzI9vsq3LdKFHtPWhXRnsg3mbVbNwZ9EinLglBMF2sABBoIAAAAMZeST+YfOtpxy9h3foTnlBNYvutxMQDwEJ8ox3Gtdn9sEBIfK0In10fFVFqec2ONkipzglBO6KAdOOZPhG+dp/a4RJTg1nX0w4Gsk4AdEOQAAED0BzpSDQ+XLPusJev+3v4u++HWPEhHR65+8TherwK34kqS8ucnXlVMORDkayiknwFPNGMEQu8BcVE45WnxVtb6j6+3iBRVHlNXijqDiqwynHFsZWKxBVP9lUc6xIcpRTjlCiNLLKaePKKeonwDKlkSLqh+mLdvjY9clHjHjq9I+TjkcX2U65ch4J1+nHM9tTomKKevplJMkRIuqXr2ccliUs6m/L9lcWQQdHF91UgojQQRgEAEAAMAEFEV1GQCnnEEUPSeGvJ1y+N5gn0454j4mJEKcsAUDKRyv94QS5Tgm3K3xVQGUe590OUoQGQ+lTBRfZbaR5koca5tM5ZRzpP87BmyRcCGd43ksj8fhhohy+Pe84VostnFIxOCU0xZfpT0MB6LjkAVxAAAwEwKcKQeHzNnRgv7eH3g7vfEVN+jDn7pPb/qzP7jvInWTLkoBwOpBe1zRIcI3fiGKcpKeDiwSjiN68FJzHfy6qy9oohwWnkhRjkXos1OEPXMfpxyuG9MpR4lkjvTvd6GEL77tlFROOev6+13xVUS1m45LPCLf7+uUo0Q5LU453qKcPTjlsPuQ0uT0OGYWVd33cUUqCn2QmLeXr/TBhYZTzoV/ucD+OGSrawAAABMCp5xRbCu+au9OOfK+JbB+EeKELRhGaJNiPpEl5t8Qyr1PegkIk2HjRozWX2bolENE45xyqv2OPb5K9aWAfntMp5xtxFeFtL+7oK+oWbKz+CrH+ouiflgR185xEtr1BwAAgN4EOFMOANEHn7+nXv/Zf/yLeyyJB+myvKFHfFWTkEU5KhZpQNmuPVH+vf8pyzo8RTm8nCu+au9OOeLpiTy3C2jSrPnUDEc1HVeOOTywsh7rlONZBywg2XBc1lEtDGlbx7LD0SUxXIxk5FUXLlFOktQWzV2DfErEtWxfbhukadW+/KRgj/6oosP6xlc5KMyns9gp5xROObFQBPZkMQAAgDiR4lz8nvRnW6KcMbEOU6A9BR5Yv4AoZz6EJjLvG1+Vb9APlYCwzZVlorbtdMphMco0m9spDTehEU45iK+aHiXKYeHXABcixFfpdJ1v29i7U44c243xhAMO2qUKAABmQoAz5QDo/J8/89F9F6GdNCsdHK4eEC3P9l2asFACghFPFW2LxBIZ5cvZY+Xf+8/zysR6q9Pq8fWO7bPIYiFEOVKI44iy2hU+8VU2p5ynP5foi76J6HVfUf5bPZEjRDKMl3vKkPiq3Iiv6uOU44iHkt9NFwPjqyzL+5RNfp7tQZTD8VVqwHlLTjnKEadlcMPplHNcC79A4GAQAQAAwBQIcW4Ik+LRUdXZ3EQ5JEQ5oU047dtFCExHaBPSPu4I5YvqD0Q5jRhuG7b4qkFOMOI7pjOP5lwRYZvIMYLBTjkcX8WinMDO3W0U4rc0SFFOoTvlDBGS2JxhigO+px8VX7Xl64DOshVCoHVg7TYXQrv+AAAA0BuIckAUvHj/kopQb8yyZR1fBVGODgswgnTKqco0pGzZkuj0UaIHLzbXoeKrukQ51XJpJlx7HEKcIOKrLEIM5aBC9UDK8pToa/8HouuVm1CbKMdnv7IBTjlUiPgqT1GOcspxCG2kYCrNyv3wjq+6XX/PxNdNir+7t/gqMXDbx7qb67WvU47W58T2XE45i1OiNZxyoiC0J4sBAADECYtzk4FP5h86fSeVNrHEV8kJp8D6RdFTCAXCJbRJMeWOsG7/3CYgOFS8nHIYef87Ip6JqN0pJ8rfMotwqS/KKSfi+CrpPhXSbw+Pq/B4jOsc0boOy+96aOfAXTJGWLPta6TOKMNCxOUF1E+BP9rvyQCRHQAAgL0T4Ew5AE2+4N0/Sn/jJ39l38Wwky7K2JRig/gqk6Djq3qKPUyuP0n6AATp6+uKr1JOOUKUk7rccfbgNDTUKccUtTREOQPjq3zrgJ/O2qyqJ4JSoqyHKMclHpHvpwuiL/x9RF/yR/3K1OaUo0Q2nvFV+3DKSTMjvmqIU45HWyfiiZ18ZRcgScce6ZSzPCFaXfiXC+yPQ36qDgAAwISwOHfgk/mHzlzjq4J2ygnQRQEMo3D+Y09UZfCOr8o9xSgzpuios3Kh+mWfe+DGaqSAYdP8LInYuWIrTjkR1YMU4oR4jlfxVSMET9b4KnlPP6BcP/PXif7RNw/4YgCMcsrZdnyVx29BiviqqMF4GgAARE+AM+UA2Hn3939g30Wwky6JLu+Wr+GUo6NEOQHHVw0V5Vx7wrXi8o+vKCfJRHyVEG04BTo7QnMt2ThEOan+ZBCRRZRT7ce6EuWky+ZnbfQVT6n4qqta1KGcclq2p+KrXKIcI1rsDe8i+k2/069MLMqxbd9HMCQ/DyK+qgddsWDadrjPbcr+opy2jPOHduMJp5zo0NoPA0EAAAAGAqeccWxNlLPlCacuCiHKCU2s1fUEO4iH0FwiOt0R8uZyIZR7n/gICAvLQymDRCdtTjmFEEwEds7ywnTKGbAPaiwpZlFOqPFVLMqpfhcHOeXYRDkjz4Gf+P+IPvbT/b8XAkGLcrquMwK+RgJ+hHb9AQAAoDcQ5YAg+V/+4y/YdxH8STOiCxblwClH48Yrywv+s8f2XZIm6YSinCHxVSwmSIUoxxlftQ9RkxFfZROrKAcVqm8uzfocG1/VW5STVE4r63pQR4lyWupRxVc5RDnyfd/YKqbVKYeFax3rUKKcfcVX5UQyLsoXH0GU3A5RU1RlogZQhVPO4hhOOdHgyKIHAAAAegGnnFFwlfkKRPhav8sqv1ckzDaIwSknsHKB/oQWx+rjjmD+PfTJPOX42/dc1dLeq4dEdz5h+Yr4jrk9OdYSY5tM4pRT7XfUTjnCfSqk8hc5ESX1WNSQuBub0GOsMKDI443eGdPOSpSzpd+NrusM6YIeUj8F/kCUAwAA0QNRDgiSd73lKfrV7/jaxvtFCAMeJtmS6PJO+brLHeXQePUXEf3JDxE98uy+S9JkrFPO9SfFumzxVR2iHBVftRBOOWnzcx7c2DXe8VVGfr0zvuqy/KvFV/k45XA7+daBiK/Kqm37uNF0ObrI/tK3PY6uV45INqccdoPpcsqpJp3SPcZXSRGML10OROZ2iCpRzqVblKMGg4RIaHlafifE3wigU0CUAwAAYAI0pxzQm76iAimKbl1uz8KTgsKdcArRRQEMIzhRjuFe6/pcLhdCufdJ7iMgNAQnXfz0dxF992+2rEY65djiqwIVEnrBTjn8vzFOOSMilvZFLE45PJ41yCmH48MndMopNnsU747EVh++5FvuI52/BUX/hwxBYCC+CgAAYgeiHBAVf/OnfnXfRWiSLuCU08a1x/ddAjssgBnslCP3K2m+7oyv4r9CrCEFITzos4/oqnLD1d8WUY7mlOOKr2JRzqr8K4UW24yvylfCKcdD+KKcchyiHFsb+ZIkpVuO7bs+ZVPrSfcUXyWccvr2Rxbl9HLKqURVXDemO4/mlGNsZw23nODRJgBiHHgGAAAQBsIpB78n/ek7oZav/Jbdt1OA9hR4YP0ixAlbMIzQnlTviiwxo5oQX+XnlKPFVyX6ezYevET04EXLemR/MbdXzMgph4add7lOonbKEUK3kMqv4qvYKWeAKMca9Tbynj7fROyUM0J8vO/4qiKvx+ZCu0YCfhQQ5QAAQOxAlAOC5q98w+dq//72f/b+8Nxy0gXRJUQ50THWKeeadMqxxVd1iXLYKUfcIGvxVSPLNxbllEOV+MHmlJOKwawOp5y1zSmnR3yVb2wSC0g2q1oApIQhHk45LvGIEkkNfKrkyTcT3X5N8/3MITyxliHdY3wVDzJt0SmH2yfflP2lr1MOUWkZDsImtCeLAQAAxIl0ysHvSX96i3I8J5K2PeHUSQzxVZhEiZ/AJsV83BG0v4iv8nPK6UmxsYseNKecdfMz5RAT2DnLC1O4NMIph+//Y3JQ2ZZTzuoh0Sf/zfj1mKKcoVFT5neniK+KqZ0lVpGS73d3JMpxCp7ENdKh/wbESpvzGgAAgCiAKAcEzW//glfRz/25r9be+0f/2pLRvE/SBdHlvfL1EvFV0dDXgcXEGV/FopyO+CoWE2jxVVKUM9LJZyzyRi3f9HDKyZrLENVOOZobUA9Rjm80gSbK4fgqX6ecRI8Q09Y7wimHiOj3/FOir/r25vu+8VVEZV3uwylHxVflA+KrPKLDGB7IU+13bF9ODoJweXg7cMqJgMAmMQAAAEQKnHLG0fP32FuUs2+nHOk6EVi/gChnPki3gRDOPz7uCPx32/EpsaCJKbraUMZXtSybb8rPc7NuC2MZoxzJXJxyBopkud/GKE6yCd2mKP/P/32i/+2d9VjzUPgBOx7nGhRfZdmvKUQ5jeMkEsb8lu/MKcexfi2+KqLjDNTAKQcAAKIHohwQPLdOl/S93/Il6t8/9eEX9lgaC9myvrCGU048pGOdcp4Q/7DEVx13iHKU6CZzOOXsWZTjE1+VZE2nHHM5FV91WS6fJNUynrnsqp18RTnVxIwWX+Xh1nL7WaKbr+wuh0u000Wa2vehjyhnb/FV3M5bdsohEqKqy3pfzXrTnvRkUU517oUoJ3wQXwUAAGAK4JQzjt5OOZ5im33HVwXtlBNgtAkYRpGHFTnU6ZTDZSwCOEYDoc85UN6Ptv3eFA73HV+nnNDOWV5M6ZQTc3xVrsdsj+XyXtlX2HF6KHIsL10Mc9awOuUU9td91hmrU86Yc+i2hcudAs0C8VWxE1p8JgAAgN5AlAOi4DOfrAUO3/tvQnPKEZPky7P9lQP0QzmfDIwjkqIcLb6qeu0dX7UQgg9RlrGiobGo+KoWUU6a1U+WFZtyX0wBhRLlXBlCJM967+topDnlsCjHw63l3/tmoj/yr9rXK8szFUqUE3B8FW9zs+7vlLNkUY5nvXFU1uaqbjcTGV/F5eHtrCDKCR4MIgAAAJgMOOUMZnB8VcckWt4hDtg2MnY3tAknOOXMB83dJIB+pkQ5DhcMmyjk0PuhPEd1xX4RkdfDKbnxwJJaT8v5tsjrB39ibJNJnHKq/U5jFuUU057juQ+N/S2V7sJJNs4pR5Zl7D19vok3emdMO29bFMnHn8uFSJ5vcO0cJ9qxF+kxBAAABw5EOSAKbp7swR3CFykuOIIoJxpYsDFUZKGJcmzxVV2iHI6vEk45QcVXeTrlEFVPBa3tdSnfk+Ij3/1S3+8RX0VUijq4jX3caNKsvc14X5OBIi4Xyg3Goz7OHiO69mT3clOzqOpvfUGDnXJ86y3NSkFVkQsBkrFN7Qk0dsqptrN+2K98YA+MfKoOAAAAINKdckB/+k6ocRRtZ3zVvif8i/3dP3WhJjYjmnAGdopCRO0E0J5dLkzyfd9jee7IyUznxKYhONHea1lnmyjH9pkaV2kpb7BM6ZQT0DHlixSsTCnKmUq8YTrlDJnEt5VlbIROsYlXUDBFfNW2BEldTjnyGimm4wwIMJ4GAACxM/Ej/wAcIBmccqLkLV9fxo1de3zY94/OiI6uE13dJ000oJxyfOOrUrvgY9+iHIazsa1OOZxLXT3lYhPl2FyE+tjmpj1EK3K5zVUzvmrMpI0SFE38s+nj4sN80491x6Jtg6wq4/qyfx3y/vWJr+IIKldUl80pZwGnnGjQBn8wiAAAAGAoPLEg3B0h0PGn74RaLPFVsh+ENlmx7dgKsDuKIrD4KnZH6IqvogCEc4Hg45TD+MZXKaewNlGOFANV6/LtS5/+INHJbaIbr2hfbpdM4pRT1UnUTjn5xE45XeIKTzRRTjZhfJVDoNNnnTG1sySfQJSzreuTrj4Ymssb6A+cpwEAIHoCfYQIgCY/9q1fvu8i2JGT9Mv/n713D7cmq+s7f7Uv55z3fbtpaGzk0iCIM2hQIggaJ5kQnUzUB53RxHuMJiZqzGRifGYGGR9nJvGGYx7z4IzJKDGIDmpAjI4ICIggiHKVSyM3m6alW+im75f3fc85e1et+aPqt+q3Vq2qtVbVWlWrav8+z9N9znv23lVr12VV1fp91/d7Ybp2MH5cvB7gi7992DLQLUcRVKBTjk08gU45G+KUQ44lKcqZsLiQrUBGU3U65eTlw6XJDSXLSAQVER85x1fhZ1y3Q/W+PXHK2Tg45VgXSwYyQtLmBmPimhum6WOoU47vNpSCKA9RFQpr1i3xVSanHNwuexblJM/QWXUMwzAMA9AsJnBxwY/e8VWW7azcp02AMpkgsWOC46uWgyjIc2ECx5nNHcEoykmg3VPiEgHiG18lnXK05bU55UhRDo4DWfbJK74T4M0/aW/HqIR0ymFRTr3clmPJezkhRDkGJ66hE20ONb4qtijSFDWmvC7SunYx/vB4GsMwzOxhUQ4zG556Qy1yePILXg37PJGbDxqvgwVo5jC4pooS6hVfRR6MO+Orppzxm9U3+SYxCv5NOuW0CFakKAe/88o/vspZ1IGinNP6s2sPN5rW5cYS5aATUMIzu9cB4qucnXLWdQRV27bpcspJTZQjBMBrng/wqfdN3ZJ0GDqrjmEYhmEAQN4HuESKdDHnwtAQlEF9h23nWkia2ilHiWZI7D6DRTnLQXEbSGB/ykLsvvt1+p4U2j0lwsUph/QhLtcaXE7DKYf2t9Qpp9oHrsfS2cPlfynBTjn1T1uMnA/yPA0oysnW7X2EbRn0Z9fvzssUACDSu067MEiUE/k+QN6DdQg0U7p2Mf60Oa8xDMMws4FFOcxs+Y9/9Impm1CChf/txbQL60x4TE45eDwcX9v9WRrltKqs901RT1PGV2UZeZgzHNs0i7jYt0c7SdERGQyIJsrB+KpdLSaREVEDBDUrQ8RYCEIIhmIj46tOe8RXoVOOqygnK2OyAOr9piMHMohTjoyvuurXvtjsrgK88xcAbv79qVuSEDyzh2EYhgmAvA8YGFX0+h8BeNnfC9Wq9PjoawE+/f7m33s75VjeO3VEkyCinNRmgbMoZzmIon5OTaGo7OKOgLAop6Tw6ANdn4GFgyinMIhyVo5xMiIfLtIITkinHDynZnRsKqKcgNe/UK5zilPOpp8ox9SWwaKcQE5AUyAG7JuxnHJaly/c+xsmTTi+imEYZvYkXAVkmCY3PqqObnnhaz8CL3/XJydsTQW6OXB01eEhRTlkkObp3wDw3/3fAJc+q/uz+BkUK9AYK4A0RDlABRJHhpe1+KpWUQ4KWshggK8ox9WhRYpyzkh8VQinHNxPkeKrUhb0yfiqM/B3yqm2fduxoZOtamHN2nDMAZidcrapOuVw8aWBMvjDA0EMwzBMXwI55dzzcYAHbg/WquR47fMB/uTfG17wFMnORZQDgjyjJHafwfeFy4HGV6VwnNmOLfr3fNf93kPBxSnHFF/Vtb9bnXJanA0aohybE1mRnoghiFMOboc5OuWgO04eto8PJloR6jhcH1GX8XsNnGhTWBxdUmYW8VUd14JUhcuMIzzJjWEYZu6wKIeZFd/wzCco/37Bf75popYQsNh8dHHadjDjc8PTamEOcs1jAJ71nfbPYgFhRR1ziOBDF7JMgZ+u9gAAIABJREFUgSKQMLiWyPiqwjG+ighbXMUtveOrzurPSreWEPFVjuISV2REU8KXY+mUc+atyfGPr1rVwhopytFWqszUQqecShSZnChnxjPAYuEbl8EwDMMwJkI55eyuzLMo5Mr+vBSr6/R1yrHd09iiE2KTtFNOwGgTZmKEu5BiDKxOOaQQO7lwLhEUcYwtvioz/M309pb9QEUnVLCjx1fZ+qyU4xaDOOWgKCexvrsLKdAQ5JwK0P5QohV6TVyt+h0/8jsaBGW4jr7LTPV47mJIHzqaU06H0DDViE/GDdxvq+2yn18YhmEWTODqIsPEJS/Um8bNKgF3CRpfxRwWz/kegL/6rT0/jKKcTf2TRvyk4JRjixKSD3OVjbJVlNMnvgrFSZ5OOXvilCPdaAZsy1jxVXK7JtCXtSGdck7Bu52P+QKA6z8X4JGf4/b+1drDKQeaTjm71EQ5PCO6CYtyGIZhmBCgUw75dx/2p8u+Thc7c1zFUuOrIOGCk61YxswHGl+VgvjLWoit2pufl30C/cyh0uZe04bLeITNKWetF1GxuOrhlJNa/9FwyumzDE2UMyehhhJfpQlVhrghhxKtiIJMCNwMFOW03DcMia9K7Xh2Yci1PPY9Ep6PrfuZ46tmD3VY433IMAwzSxKems8wTf7Jf/258NQbLsl/7/IEbkCkKIfjqw6O9QbgwqP6fZYKVACa7jEpiHIgA9hXAgl0PKFIpxxbfNVGff9q5S5ukYIe3/iq89r+OOn4qrk55XgOLD36qQD/4r0A13622/upU47cby5OORhfddWvfbGhdtZMSZvtNcMwDMP4oDvl9GV3td3mfwnkO4B8TFFO5FngLiTrlEMcFZh5IwoSk5ZA/yEL+IZzHV/H52qOrypRXD8s8VWZoyubsIhyVtuW+KqNfdm4/OQEK/o2GuCUM8v4qjZRzsDvEOpaSl2ysnV7H2Fbht4WIYb1gVLAltrx7MAQ1zsRW5TTMSlM9meOzlxMmkinnM28+kqGYRhGknAVkGGaXH/pCF7/g8+duhkqWFTfXup+H8NQMoNTjiLKSSG+yuaUgw/hNlGO5jKjf9cu5DJ7iHLQaaVN3OGDjBkLLcoJ4OITG9x++9P47VQi07bm98hBUzL7bH0EAFl6TjlzHmyKBcdXMQzDMEFApxyOr+ok39XOGBTlemwT2hTdhR5luRMKT2ShInWnHC6izBp5nCUkygGLO4LIa8HD5G5WiUC3VZerBAA4j0W4OOWYRDnS3cuhL+4jqohJwymnT5SRHl81o2OTOtqY9m1fgsVXEVHOatNveSYhiRgY4TfnOMchwhoptop0f9Ll4tPobxK7R2LcoE45PM7IMAwzSxKuAjKMmXUKkVUUdsph+pBpIo8nfhnAY59BXs/Un5OQ1QIJkyhHccrJ291vdLebbO3+vfToKyvVcvdnpZMRQO30MkRQQwcyQrIOIBiKDQqH9mcQPWYrW9dCsLXhmAOoZ7NTp5wsK/vgfWKiHI4paBJyBiHDMAxzuDSccvqKcq4u+3pU7GpnDIrP9ZgWgW1FnCkL/r4F7rFhUc4ykMdZIhEgLgI7UZBnd4/zecn4PpNkDtcam1POeqttf80px3YdSzG+KqhTDjoGzaiPjOWUI4+lgE45fYv4JgHN0Ag/MePJS0Ou5bHdBLtiz5IUlDLe0OsG70OGYZhZwqIcZpb89Dc+w/6msWBRDtMHPZ7q234N4Eu/p/31KaBRQiaBhOKUk7eLXhrxVWuP+Cp02fF0yil2ceKrXNvtyiziq1CUczW+eCgjkWm4Xl0IJB88tZz2zUm6opwlx2J4I1p+ZxiGYRgfAjrlzLEo5IIQZQHI5KzQW5STcHyVjGZIPb6K7wtnTWqFTbr+VqccKsrZqX8/VFyccnzjq9pcUqkoR4nN0o8ll/iqxPYZbXMG/a7FdPvQf88BRZTj4UBnI4pTTsj4qmKYMHHOk5eGOAKOJcoxxlehoHSgmJ2ZGI6vYhiGmTsJVwEZpp1v+pIbp25CjRTlXJy2HczM0OKrGi+nIMpxdcopLPFVmtvNau3+vbKs/LzP+xEpeMkAnvRfATzm6W7LMEEFRSGZRXzVmE45qzqCCterI+2CtUGEzUl68VUi0GDakmCnHIZhGCYEQZ1yFnqdRocco1OOR5ykjyhnSKzDYDRRTmouICzKWQa6u8nU+1MR5ZgEeKSAp79n6rZPifJMYrsGOD4DywkZLcKHVYtTjqu4ocjTi69S+t2hTjlzF+WEdMoJdL2gopysr1OOYaIRFfr1coyZsVPOEEfAIvJ9QOdxQ+P4ekbNMdPDTjkMwzCzJ+EqIMO0k5HC+xs/fOeELYG68H/EohzGAz2+Skc6xEzZTWe184hJlCMHvHOLKEdzmcnWfuKW1QacB8Lo9sJzEwDgu18L8Ixvcl9n23JDi3Lkdk05vqpq4/40vlPOak3cmVpEOXLgRnPK2Z7ULjup0GUffKj4FAEZhmEYppUATjn5HiA/X+6gNjpiFAZRDnjM6FeKyJZ7mikLbdLVAu/XE7vPYFHOMpAFsUSOM5u4RBcR5YFEOfk+PdcWH4SDUw7dty7uErL/a4uvOtKEDZpTTqj4qtf/CMCrfsD+vhBQN6GsZ6Efv9PcnHJ0Z5yQohw8hoZeS0VRH7urTVinHFeHJ+MyZzx5abbxVSTiM+spoGOmh96DzPH8YRiGYViUw8yfN37kM9M2gJ1ymD5kc3DKAeJa0uWUg6IcS3yV4pTjKcrp45Sz2ra/zxdsb9v+6gt180kVZd+P4JSDgwPSRUiPr6JOOTS+6kLl5pMQc7ZljoUyAMUDQQzDMExPQjjloJh3zoXlLqRTzojxVZMKT9gphxmBhrtJSk45XaKcwPFVv/y1AH/wY/0/PzV0W7U9qynxVfi3jmUKmyhnY+5PXR1HhKNTzqc/UP43Cpr7xiCnHHSfEgBv/FGA3/ye9s+kgH4dDSrKCRVfJcg43Kbf8ozOMGKYW9icJy8NcQQcLb7KdC2gArrV9Ncuph/U/Y73IcMwzCxhUQ4zW372W78YAAD+6o3XTdsQKcq5MG07mHlBLWS7Xp/SQSVb1cWKzYnhdRw8yquZMpb4KuqY4yM28oq7anHKGcrKsr/6Mof4KupYE1s8pOw/F6cc8v7tSR23lgpztmWOhsfMfIZhGIZpJYBTDt43LFU8m3c45cQS5cQuOHUhNFFOauJfJeYksbYx7jSEFDNzygkVX3X/bQAP3Nb/81NDt5V1O1ABaAdt7iZyH2zN63WJryo8RAyujjohiOWUc+efAdxxU5g2xqIhyvE5pmzLDjSOQOOrVgPjq/TvO0SYGDvGKSZ4jKcmyqHnnnE/a1FzU1+7mH4o8VW8DxmGYeZIwlVAhunm6Y9/BAAA/ORrPgLf+ZJ3wps/OpFjDj44slMO44MtDklGPU0dX1U5j5gEEnIWauHolEPcgVY+opyNuxiEbq+QrjbRnHIqF5q5OOXEPh6p6Ekec7pTDg7gaPFVm5M6+ioV5IDNQot9feD4KoZhGCYEAmCwU87uSvXRGRaFXEAxTm4S5fSNr7K9d8AM8sHoTjkTNKELpaCZWuMS4JY3A5xfnroVDmiRQ1PvS8Upx3DeNeKrAjnliHzeEw8Ky3YDAAgeX7VtccpxcBzxcdoq9iPum5BOOUSUk+9aohcTotMpZ2C/EOpaqsRXBRblhHDKmeP916D4qoj3SDaxtfwbCtr5PmSWUKecOV+DGYZhDhgW5TCz5dGXykLxA1d38JaP3QX/8JfeBbt8ght6jq9ihtAqykkhviqrZxCbnHIa8VUWpxwpbPF1yvGIr6ICjjanlT5IEVXg/SHjqxK+HCvbcUSnnI2DUw5oopzUnHKkLfMMB5tiEdLWm2EYhjlgNHHuEKecpQ5qS6ecMeOraMzoyEjXiQFCrZjwPVA7V+4F+JWvB7jplVO3xI6PkGIMFFFOx7kunXICuXkU+bwnHtgchgDAGF/lssw2p5z1kXn7yzGhjj6rLRrLRF9Rzs1v9P9cEKccIloCqARfO7fvOiUxRTmhRCvUKSdb99umRlGOIGNjA9yR5nj/NURYE/MeySrK0Z1y+D5knhBhMO9DhmGYWZJwFZBhurlw1BQz/Mqf/MX4DeH4KqYP1viqTH3fJGT17CTqliJfJna1TqIcIjTyiYE6eSTA8bWOTY4VXxXLKWcG8VVZRtoZW5TjIKqiAxn0/dsLtbNTKsi2znCwaRQSK5YxDMMw86FRLB3ilLPQ67SMUhkqyqFFZMt2ntIpR4+vmtrBRIdFOe2cPwwAYh5OOXrk0NT3s4rrVVd8FU6oCRRfNaobSwTotnL6Hg5RiTanHN3ZoBFf1bE/fGKRi73/de0zHwZ42d8FuOVNfp8L6ZSDz/+iqCZ+JX58NUQ5AWOa26LQfFHiqzY9RTmGuKahTjnFjMdJBjnlxIyvogLNjmtBlpXHRGr3SIwb9LrB95IMwzCzJOEqIMN0c7xpHr5iiptKfBA5ujT+upn5QqOcWl/P4osgulCigQyiHJwZgwMmbUIb/I44EHjdEwGue4J7O/7+KwCe+0Nu740dX+UjJnJh0xLRlBoYsxW7nStDfBUeh9iGVqecY4B9qk45MxxsioUymDddMxiGYZi5I+r7ZYBhTjlLHdTOz6ufpvgqD4EI/bztnial+CqXG418B/DaHwJ4eIQobBbltLOvjtXU42oASHSEg5BiDJwLsSjKofFVA27GRT79dx+CIo5p69eI4CTT/2Z6uy2+6khbFx5LDkJCKQRwEeX0ELScPVz+9BXGBXHKQVGOFl9lunalhH7uhezjQ4lWFFHOut/yBBl/kQId4SYm62oXwDzHSeYgyjHuZ3KPxPFV84XGV81R1MYwDMOwKIeZL5lBrPC0xzq6aYQEHxzZKYfxgT4Yt+Eb8xQaxXWmyymnGvRp+y66U87X/SzAN/6Sezse9WSAi9e7vTeWUw6dXRSSOTjlAJCYrcjroQ5S+vG0rSLU2pxyNhcAdqeRG+jJnGeAxSLkDEKGYRjmcBGVOHeIgJ2KcpY4Y1jGV5kKm4I4ysSIr5rg3qchlnDYp/fcDPCOnwf4xFvitQthUU47eeV2mXoRHqDpPDP1vnR2ysH4qlBOOcU8C+qIi1OOb3xVm5sNLme9NW9/F8cRr/iqHtFiKOL03qcBnXLodij284uvChUNB0CupQHjq1brfues6do11Clnzo7CQyKoYgqXpQBzZV6+0M7VJd73mhAC4E0vBLj3E1O3JAw07m/q+w+GYRimF4lXARnGjyvnE9zQc3wV0wuMp+oQ5WSr6eOr8KdJ4CItsHNLfJXmMrPehBXMUFzij/og46sCO+WMFQs1lM1ITjl4vJucmTZVH9vmlLM9YaecOaAMHBzIQBDDMAwTgRBOOVfI4hY4sI1inLwlvipzFK/4FPFTcMqR94eOTjkA49yrsSinnX0lrE+9CA9AhBoe4q+YWJ1yyKx6ALU/GBpfNceCOuLklENxuNZYnXK25jhAlyg0Kfhx2GfF3u19lL6inFhOOcUufecspU8XcZxyQsZXZaFFOdgH9mgXHp++x2kKpO6Us9p2XwtwnHnqa9dYXL0P4A9/CuCjr526JWHg+CqGYZjZw6IcZlG88cN3wpNf8Gr46B0PjbdSKcrh+CrGAxennKlFOSgU2ZyYRSPonpOfWUQ5mlNOTJT4qhhOObFEOYlfjscSD0nhlmHfSaccHATRnXJOAPZncdvny5ABm8XCTjkMwzBMAEQBwZxy5PIWRpdTjhDus9x9Zv+Hmt3fB93VwqXghNtmDHGBzdHkkNl3RK2lho+7yRi4xletTPFVA9qObrlzRdlubdtBF/rRvxmQQgNdlIPCKF2U4+OUg7FBLvFVPQRT2GZvYVwIpxwiJgAov2u+T//40h1gQ4pyfOLKbMuhrs99hI9WUc6A+Kq5XQuLgft4DKec9da8XaWYA++dW87VO/8M4M0/Fb59UyGFUJGPtT9/A8BNr4y7DgCQ+221PhxhFcMwzMJIvArIMG788Qu+EgAAXvHu2wEA4H/6jfeNt/LtxfLnySPGWyczf5xEORPHV+Hg06bFcQZFErtTt/iq1RiiHOqUEzBqis4uCslcRDnoXBO7nbh8U1yazSlnc1IW11J6MGWnnCb6jEKGYRiG6YMU53q4ouhQp5wlXqtR4GASOvhET/gU8WPOArdCisPZCpyOCXQNYaecaZFOOXMS5aQSX0WPK8NxrEcDBYuv6hGRlBK27UbJHAWgLk45wiTKcXBd8omv6iOYQqcc330azSlnn75IT+/Tgzrl4LVpqLhHqKKcPuesUZgriFvYgPiqud17Dd3Hozjl4L2dfi5S4XLHufrh3wV48wtrsezcKUa6z3vHLwC87WfjrgNAvZ7M7fxhGIZhAAAgYMWSYcbnl/7Rc+D3broDLh6phfIP/uWD4zXiv/hvAb7lZQA3fP5462TmDw7qtLnLAKTllGMCRRL70/Kh2ibKmbNTjoyvCnzZPHlEKey79Flhlxua9cjxVTR6TB6HVRvkrCpQB0e3J+Uf83Nz/NUUzHUGWEyUwR8W5TAMwzB9qcS5Pq4oOopTzgKv1dQFRncYFMJdVOAVXzWhS6BwLDhRRnXKYWFyKygIMEWtpQaNjij/MFlTytU7OuWg4CEP4JQjRCX8mLG4jG6rtsKmcp46XGvaIoeUIqqhP8Vn4D7Lbnuvb58m46v6noNUJOuJFI6Ra1KxSz/OThd2xYivCuG4I8ceI8VX9ekD5zpOMmQfY7/Z57OuywcgAsxcnago15lVplYtbRjLWWYs5LkU+fsU+3H6LI6vYhiGmT0symFmzVc87THwFU97DJzuJrxZXG8BvuDrpls/M09cnFcmF+V0uJYAEKecq47xVYFdZoxQp5wWh59ei0VRTuDvcHwtwL+8CeDC9WGXG5rNWPFVHe5M20oEJh+mdaec6vXd1fREOTyDhcDxVQzDMEwAdJHJUKecJV6TqMAh36n3Vz7RE1hkyFb298qC0wRCBblOdGxw2KdjzaAGYKecLjCClp1y/FFEOYaCoO6e4BNHZ1vnnIu2imON7Xs4PgPbnHIa8VUkhqT8Q8eyPZ4rix7RT31dw0I55VCXaFGU7TEJSlMiplOOCCQkUOKrAotycLxySIzT3MZJTE5XrpjO/ZBI8V91rydyUMp+8lzFceaWNvi4cs2BUK5TNkQ+jrsXFV9Nff/BMAzD9CLxvAyGceNkO0axn2ECcs1jACDrdkjJsmlFOaA5lOhQp5xOUU51fo7tlBMyviqWUw5AeQyMEe01hLGccnA7mwRV6NhUkGJPwykH6kH9FBhrVs6c4FniDMMwTBACO+XMrTDkQk6iB3Sxg1d8FUaLHLu/d8r4Khlr5hFfNUZ7WZTTDt6/px5XAwDyuJKinOlaUq7fclzpbiwh4qvmWlCnuDjlIEp8lYubTVt81VGLU46DwMtHpFHsBzjl+O5TIoZ07Xd1MAqdinJkIT1hYUCXKGcooc4xKsrJ1v22J72/UsZiVlC60g2Ir5rbOMkgp5wAgsjO5WM/YxBg0tdtboJt/dhcGUtEWhTjbDO6n+d2/jAMwzAAwE45zEL5vMdcM3UTGKabxz8T4H/5OMClR7e/Z7WedlYQrrpNlKM75bQ54eCAf2iXGROx4qvkQEbi4plYjOaUY4ivwgPR6JRDQNHO/iokAzvlNOH4KoZhGCYEUpzrUChtQ4mvWqBIggpxdLEDFeXY7lOwyLA+cnfVmeLeR5kF7hlfNUZ7QziULJV8jk45jqK26BgK5srLulNOgPiqJcSbuBTXTX1IV79ic8pZbwBAlMXb1cpwLAWMr/J1hUBRju8+VZxywK3fbSzD4JQj++Z9Hb2WGrNwyhH1dl1teopyWpxyIOvvlCPHSabuPz1RnMl8nXICCCK7oI5cxnXo90ht/d4CRJeUse5LR4uvwv3I8VUMwzBz5UCri8zSuXTEzjnMDOgS5ADUD0uT4eOUUzjEV43tlBNBlBPDKWcOSJHMFKKcCumUgwNBmlMOvr47jdc+X5RBKwYAyLboOauOYRiGYQAgjFPOAcVXNYoRwj1+BwU96639vbKQOKFTDgC4O+VU320McQE75bSzr+7f8xnMzPdxNxkD5bhyEeXQwnBPgfwSirZ0W7V+D90FxgI+p5r6WwAtVgbIvnFxysFlO5wjRd7DKYeIYLzo0e/qFIXqlFPkdTtSds/SBRoh+/hQrnN6fFWfa53i8CLqn+js7duPFJY+K2WGiGvxmI4VO9QQ+bU45aCYqu1cLTz6mjkwloh0rPgq6dbH8VUMwzBz5UCri8wSufWnngcAAN/3/74b3n/bA3D/lXN45EVDYZdh5oLM+p1w/QAkukhjvSkfBGR8lcUpZxRRDhksCynKkfFVByr4W4/llFNtX5MQzOiUkzVfT8opZwED1sEhlv8cX8UwDMP0JbRTzhKv1bb4KikqsGw7L6ecCQXJimNDgk45SgF3gcfbEPYYnZNwAR4R5F62/MNkTSlX7+mUkweMr5pbQZ3iIwxwja+yOeWgg0WRqyLHzOFY8olgKfb+fUzfvrBPv9tYBjrlEHFSPqP4KnSLiSHKCRlftdr0W16bU0627eeUM+dr4ZB9jN915SBw7oN05CL9jPI6ccrpjK+awbnnw1hxi8V+nHsYek2fm9MUwzAMAwDslMMskOPNGu548BS++EffMHVTGGYY1MJ3kvVbnHIASneSHYpy2pxyqsGVsZ1yYsRXHapTDh4DsfehFIKRfSePQ3TKoTnmBqec/VncNvowVn71nGC7XYZhGCYIoZ1yFnitdo2vsgptqm2zcRDlJOGUg2ItF6ecEWN46DHK90AqGF+VsisG4uNuMgZKgdtQRBVkVj1AoPgqLHLO+Dh2ccrxja8qLKKcteZWJPfNSn1f17JF4Sak9C2oo4izt1OOR7+rU+TlNpDxVUKNr0oVWhxHUU5rdJDvsiM45WSrwKIcSwxS6/JmHOU4SJTjIXDugxT9tNzbSREZ3ju3nKtt4sK5MpZTDnX4igm9B5nb+cMwDMMAAItymAXyO+//1NRNYJgwTO2UA5oYwsTmpHQmcXHKGcNlJlp81Vr9eWhIt6QJ46usTjkXy59nD8dqnT84YDq3GWAxUWZoMQzDMExPBGgOfgOdcpY4sE0FDg2nHOEhyvFxysHix8yccsZoL8dXtYOi+jkUAXWRy9T70nZc6SKiIoBTjhQMzPgZR4mhscVXATg9B7c65eAxs1XfB/qx5OCUA2B/thRD4qumdMoh4qQ5uHXQvkAUlbgoUL8QzClHaE45PbZnqyinb3yVy7mXKCGcclyiQPugO+U01kHGYbocjsZylhkLOqkvJtThK+p6OL6KYRhm7nBFhGEYJlWmFuVIh5KOGLjthaqgITqccjC+agxRDhksC+mUc+jxVZuR4qtWJlGOo1PODU8rf97xgahN9GIJ1u6hUWb2cHzVwXHHTQAPfnrqVjAMswhQnDvEKWfh8VW0+KYXCoRwd/qQopxjd1edKa7xsm001syCLPyyKGdS9jN0ynGNf4uNTawRM75qzv2mb4SOS3yV1SnnSH1f41jq2B/CUcggRL9+Dc897+fWUE4563obF/t6W6TcJxidcgKJcoI65VTbdbXpNy6hXLtIu+R4pec+X0J8VZ/YrthOOXpf3xZfhedq27VraU45Y0XKTxFfxeOMDMMws4RFOQzDMKmCM0+ma0D5w+aUc/ZQ+bvNKWeU70IG4GM45RyqKCcJp5zqOGxzyrl4PcCjPw/g9nfFbKEf+MA8Z2v34JAZWlyQOjxe8Z0Ab/k3U7eCYZglgOJcl0JpG0uPr8I4EgCDU07hIcqpPrve2u9pUoiv8nFs6F2I7oFS2GRhsoJ0ykm4AI+kGl+Vrc3HcaNQG0KUM2LsWyxcInSoC4x1eQJkH9QohutuRVo/KSc3OQh+TMs3rQvAb//0ja8K6pRTiQWUa1fCwgC9OO4jdrURanKPKECOmWDcje/YBH2//F7iMOOraERUb1HOBgBE+PuAhotbiyjHFl+F+zvlc8+HMeOrxhAR6mJAhmEYZnawKIdZHK/4vi+Xv18+W8hNJHOYrNbTinK6BBLI9gTgvIoLanXKGVHQEi2+ilj+HiLSKSfyenA7b46br22q+CocJNCdcgAAbvxSgNvemU6hQ9o7z3jAOjSiElN1DQQxy2V3VXWmYBiG6U1gp5xU7h1CQosDeqHAZ0a/jFywzO4WYtp7H30WuMt9hnSUGFuUw4UUhRydcmYwfqMLKabelzSyxFREjRlfNeeiravIBQCcrjXK8gxOOdmqKYzyEXgp7iId252+5tOv9Y2vCuKUQ0Si2UoT5ST8HB3TKSdYfFVBxrLwOPONKGuLr1pB6bjSM8ZJ/30ODBFE0HspgAiiHC2+qk0cKPebzSlnZvumjbHu80QOAGKE7Ybiq/X09x8MwzBML1iUwyyOL/mcR8nff+5NN0/YEoYZSDLxVV1OORcAzi+Xv1vjq0b4LnQdIeOrTq4DeMpzAR7/rHDLnBOjOeVUA0VUUCWPw6oNygxsXZTzbIArdwPcd6t5+e/6RYD3/mqo1tpZ2oBGCKSFdo8BPGb+FDmL1BiGCYMQVQ1woFNOm83/ElCKs4YisbMop/rsxiLKodtwUqccnL2foFOOS0zNITJHpxz53JlIfNVqa4mvQlHOrvmaL1IwMOPj2MlRxrRvLYVsgA5RjiaKaIhyhrYXtH7Yo1/D42Iyp5zqfMpWdX9A25UiJlEOjmMM7RbkMRJQlJNpTk0+y9B/p/FV3k45ZOPM7bmQ9re+x7rQRTmB+0/aNuPySXuzVYfAcK/+nDtjRcqPtd3YKYdhGGb2sCiHWRzrVQbXHpcDjHc/dGZ5N8MkDA4oT9eA8ofJtQTZngCcVU45WQLxVXR7rQO62qw3AN/1OwCf8+X29y4RHDiIfTxKdyaTU86xNuhjcMp/Bx7vAAAgAElEQVR54peWP9sirN73awA3vSJIU51gpxwDwq9YxiwLHLBmGIYZjOaU04fdVYCja6rFLfBaTd0GGpb6wl0gIiMXbE45E4tylKgZV6ecvu4QPVAKtnwtVMAi/BjRD4PRI0ISEeWsWwp0urNPHiK+aqQiZ0yK3C5SaMS9WJYnP2eIjelyynHpixV3ka739XXKObe3wUgIp5y8wyknYWGAUhwXcZxyhvYvilOOIcLOdRnyd0F+Zt3ijtblzdkpB+OreriUyHupSPcBNhc3KijNAFrP1VAuTakw1vfBfjn2fYweUzZncSzDMMyBwqIcZpE87bHXAgDA9dd0xO4wTOo88kkA1z1puvXrDiUmNhcc4qsmcsrpit1i/JDxVZH3YVdk2vqofF0ZoNIGR2/4AoDtpXZRTpGPWwRZ2oBGCNDhKFvB5DOLmfFhUQ7DMKHAGEu8X+0bX3V8bfX5BfZNtDCguw0IEhcSSpSD9zvZVJb6pDicgaNTDhbHRxLlyGIZ3xsq5DN0ypHnz9SiHCzQtTnlYBHZUJDv2/YluIGKgjhWBIiv6hIayOcfTQSkH0tdz0Z94qt8+pl8aqecFlFOypF2dP+JQnX8GXoNDHWOBYmvMghu6T2Yt1OOo+tTiuDxvd4OEOVEdspZtzhA6sJla3xVwueeD7oIMvZ6Yt/H6KKcJT6/MAzDLBwW5TCL5IV/94sAAOCpN1wzcUsYZgDf/hsAX/UT060fH9674qu2JwBnD5W/r9qcctbdr4ckVnzVoTNWfBUeIxsqyqnWud5WRR4cXDA45aw3AE94FsBt7zQvX+TjziSRM8lmNtgUE5wpyvFVh4nI513AYRgmIXSnHN+Z2qKMrzq6VP57iX2Tc3yVZdvlZHa3i1NOn2JVCAY55YzQXiHchVCHhnTKmUER0OZGMDZKkdgkyiGiHYCw8VVzfsYReV0cb+3/PeKrFCcbS3yV7oLi4pSjCCMc46t8+jUUwngX4iM45SjxVQn3CfTcw8k/wZ1yQopyesZ1ho6vUo7RmfUh2N4+0UF4LLfGSw2kEV+lb1sa8dmx34qFiXLGEpHiemLfxzSEwXw/yTAMMzdYlMMskusulDehz3/lB+Dhs4XcSDKHxyqR+Koux5nNBYDzy+XvyTnlsCgnGJux4qs6jrn1UfngKQeoAIwiocc+A+Cuj5iXX4zs0oHrYkvZGlEMm0nJzBu0dmcYhhnKUKec/Lzsjw4mvqpLlOPqlHNsKQY7OupEgzrlOEZqFGM65eTpCDlSYz8npxwPIcUYUJGQqYiqF/AUJ5W+ohx0BJhxv1nk9hgZn/iqLicbXZSD/Q2Nk+lyrsD2ti1fWZejeEcn7xnlF8Qpp2h3ykm5T1Diq4pIopyhjjuiHiPDn0NEObRdKO7wFkVPHHU5BN0dyQfcdlPHVwH2Z21OOTiGtZBaylj3eVLMFLvPQqFtT+crhmEYZnJYlMMskkderAu6H7vzoQlbwjAzBsedbE45+NDR6pSDopwRnHKw0dl6YkHTwhjLKccUX0WFOkocgsEpBwDg+BqA/al5QFCMHF8lAs1wWxSi/wAeM384vophmGDoTjme7K6UP49RlLPAvqkzvgr8RDnZ2l6EQhGyLDiNfJ1vFNBd4qt6FqL7ELJguzSwCJ8nXIBHZOETh1Onjq8i550xvkor1BYBCuK6qGSOCCLKsZ7/DvFVnU451fOPHiEmqHOFxUXUJIwwtmPv9j6d3n2h7pTTgyKvz6fVDJ1ycIwiZB/f5e5x/20An/mw43JMTjm+EWVtTjkogB0QXzU3YR91o+ktynGNzfPEOb7KIgJcguiSMlak/FgOQ6m59TEMwzDesCiHWSRHm/rQ/v6XvUf+/sc3383OOQzjTDWosrE45SApOeV0ufsw/mwqUU50p5yO/bfeloN0it23oT0oItufNl8r8nEFMnKW0UIGNEIgSBGVBxAODxblMAwTiqFOObur5U90yhnD1e6hOwHu/LP460FoYUAXO4jC3fq+2Jf38zb3GbzHWrkWugMjtOKwy/VmVKccj8iwQwPv21MuwCMyDiqRgpitSKwX8PIQ8VULeMYp6PnoE1/V9taOSB7plKMVy6lTjm3Cgqu7iOKE5CPKOff/DADR5Axxysk7nHIS7hN01xQhakFEzPiqP/gxgP/8ve5txPuk3qIcsk/1+Cqbw5MJRRg4sz5E3ucMiK+SopzQTjlaVKG+bWV/g+fqgcRXjRW3KOOrIouLRQHSEVL+m2EYhpkTLMphFs+dD5azLO566Ay+/RffAf/sV/904hYxzEzAh3ebUw5iE+W0OemERIo6OLoqKFIkE1uUUx0jKALS25CtycN0i1POthKKGUU5+5GdcnDQamaDTTERxCmHC1KHRzGyWxXDMAtGd8oZKMoZ41r9lp8G+LVvib8eRHHKGRhftdrYnRxiRzNY0V0nUnTKSSTyKDX26JRz3v2+FEhtlrqzU06E+Ko5P+PI+J11+/lvjK/q45SDriJr9b2ySF79z8WJzLT81nZ47B90U/MuxOtOOT2e74q8Pj51UY4evZgS9NwTefU9AvQLQhCnHMNyzi/XEfIubZROOT3jbozuWvSZ3tcphxwjcxP26ZFlPuC5hZMeQ4+F0OOR/rt+Q/nDJgIUSxPljOT8I9cT2ymHOk/D/M4hhmEYhkU5zGHwinffBn9xT/nQ8paP3TVxaxhmJri4zihOOW3xVTjAMkKclD4LiAnD6E45VFRF4qtWZNC01SmnauvOIMoZO75qLKvcWUGcDViUc3iwUw7DMKEY7JQzQXzV2cMAD35qHFcegLKwiTOmjU45vqIcS/ENCxFTiRVoAd21OIyF6NGccqYSLCUOiunnFF+FAoup72cVUY6hGNiIrwrglNMVrTMXRCUEWa39zv+23S1sopxVUxShO+V0OpG5xlf1dCHpK1DUhUsH6ZRDHJeCiHIMcVGUIm9GUnYtqxFf5buP2+KrPASwyvLm7JRDI6KE33fH7R7rPqDR1xfm16WboEVgmPK554Og44cRwe02hlMOCkrx3wzDMMysYFEOs1j+1df9Ffn781/5ASXSimEYFzydcrI2Uc6m+/WQYGGGnXLCgtszdgSZFOWYnHK2BqccwzJQKLa/2nytKMYdPKaDVkyJtNDuOZOSmTdi5HOQYZgFo4tz+zrlXCp/jtE3FbvyPub0/vjrAigLKkcX63UrCL/4qrWDKAfv0WJFM1ghx4CzUw7ObB6hrVQIxddClbx0N55FEVB3npn6Pl+Jr3JwyslDOOWMFAcSk6ISgnQ55TRcYCzLk7/rx7FQRTnydd3dy6F/1X9vtIOs26efQSHMIKcc+m8PdKecPRXlJCzUa4gg9mEiCm3CKpG7OwhRUU5fZw1FlCPU5fZxyunr5pQChbbPffZz7Hskk0hMeb36if1Nq1MOxhPO4HrswljOblKsOoYoh8ZX8ZgawzDM3GCVArNY/uFff4ry73/zuo9O1BKGmSkyvsogkEAUpxxLfFVsQQddR5e7D+OPFMlEdsrBwTjT/pNOOSh0aXHKQaHY/qz5msjHfWiVAxozG2yKCe43jq86TMZ2q2LS4qE7Af7gJ8ZzCWGWTSinnKNrq8+PIcqpCgOX746/LoBytu72Yv07pZdTztrilDNxfNUgp5wBbX34MwAv+Zqyj+tsn8c2PzRkfFXCBXiEupvQf0+Fc3wVEQ7or/kii5zF+Pfzt70T4GV/b/ixojjltBWnPeKrupxssIiqi/J0p5yuPqtT9APm17xEOUOdcmCAU06hOeWQ5/iUn6NN0XDSPXrAeSEsopUidxdMGJ1yPMUWRqccfKa3iMmsy5vZeAAVQdJ/u4DbPdY9kh5fZeqHAGpBR9v6x4p7Gosx3KuFGHHsTxBhFUx/D8IwDMN4w6IcZtH85vd/ufz9rX8+0uAnwywGB1EOdcqxiXLa4q1Cog84MGGYMr5Kuh8dGZxyTPFV1TG5MznljCwIkINWCxnQCIGc2QM8gHBo0JmVzGFy8xsA3vLTAA98cuqWMIsgkFPOmPFVOLP98khxyvk5wLYS0JviVHyccjC+qqvYoBeEJnPK8YhRCTGD+jMfAvjkHwPc+UFL8zzciQ4NjK9K2RVDUh1X8nlz6qIybY8hTqUzvqpn223xOjH55J8A3Pz7AFfvG7acIq8jQLziqyyRLwDt8VWZ5pSjCLwsfZYi+nGIEQTw+1553yg/3U2or1MOcXOh8VUpC/X0c4v+PuS8cHHK6RVfpcWnuaKc70RQ1tcpZ9bxVRhB1WM/S1FObKectnswTbh8KPFVYzi70XN2rPiqvuczwzAMMzksymEWzbOe9Kipm8Aw80U65XTEV9HX2kQ31Io4NiZRBzMc6VwTW5RjEILhoML2QjlYJx+qW5xy8JjEwX2KyMd9aGWnHAMCnGaDMsuD49yYMWYqMoeDAOKIAgOccjC+aoS+qRhZlFPsAbbV9+t0yrFsu7ya/W8rvuH3kwWhkc91OgvctTjc1x1CWUb1vWkh2QQ75bSD204U6bupNSJCJr6ftbojaCKikPFVpvXFZlc945lcUX0QohTJrLrEhh7xVcJBlKPHyuhOOZ2iHEchQ5djTxd946t0N6E+50Oxr68bjfiqhIUBuggCIJAox3KOFo7xVUKAfPambfPex/TYI045MkLHc5/POb6qEV/o0f6xnHJa46twfdT5y7ScBcQTUsZw/lH6/9iiHOI8DcD3kwzDMDOERTnMoskMN5rXnrCDBsM44RIF5SLKwYfObASnHBwsW7EoJyijOeVgfBXZf5//PIBveinAdTe6OeXgjHCTKKfYs1PO1IgC5MA2DyAcFrpVP3N4CBblMCEh0Qny3x6coygHnXLGiK+qBuqvjBlfhU45uihH1PddPk45XQVXGV+Fs8BHFisIUkDPMrdDIoRTDhazbSIBUUzoIpQ4+9P62TN1t5xGUXZqUQ6KblCUYxCEAKgRO/prvvR1YwnBvnI5s4ngbIjKncXFKQddPssPmt/j4pSDbjCNe2KHGCAqVnONr/JyykFRTmSnnN1pUySK1xiAcjvkcxPlkHGuEKIcmyuSa3yVFEyhCxH2AUPipsgkC+nw5Ls8cozMbZxkiCNg7IhP2TYtJk++To+Hjv22OKcczZks5joARnDKoZPcgO8nGYZhZgiLcpiD46HThdxYMkx0HJxycKAfwB5fxU4586VLmBUSuf+IU87xNQBP/4b6dT3HXAcFRDuTKKcY96GVDrhOPWCfCnIQoedMSma+sFMOM4Z9OHM44CztoU45x4+oPj+mU85IopxiZ46vwtnzrsVDdDGwOeXgub2eyg1Gi2YYyykHRSQuopxU3FVSIq9E8yiQSzmuBoAUNhOJItMLsW3uCCaXjL5tV1wBZuqUU+SVU866/Tv4nKedTjlCdcqRRWJSJM8sfZarAw5dt4/4QrpJRHbK+bVvBnjdDzfXTceMZiPK0aPsIExEYaj4KiUeDczCPBcUEY0myhkaXzU3oT62t1d8lS5cjiXKaVk+dRPsOleX5vY8xqSQMd2f6Lk3xvoYhmGY4LAoh1k8z3zSI6duAsPMExkl5OqUYxHlrFiUM1tGc8qxuDMpg6YtTjkbdMq52nxN5OM+tCqzyrj4AgDVIEJPq2tm3ixtgI/xB/vBlAsszIzQnXI8Ob9c/jy+tvw5Rt+EcRNjxVfl5wDbi+q6AUgh0dcpx+bkgMWqidxg9AK3y70XFjaHtBVFJLmPKIcFqhJ0t5RRcqmLcnSnnEREOau2+CosxIZ0ynGMUoqBdMoZGl+VV7F8XU45hviqtn6F9n+mfZBlZB/QOGZwcxxxja8a6pTjfUx4OuU8+CmAB25X/1bsa1FZtlYdb1O+Z9QFb/T3QU45lgI/OuVYr3FUqApmYZ5Te9qccjK7g551eTN7LmxERPUQvkVzytFc0xrLdxQBLs4pZ4RJIYoYMrZTTlF1t4ncgzAMwzDesCiHWTxf+pTrp24Cw8yUUE451cPCmE45HF8VFnSuib0P8VhpE4Jla3enHNPsySIf96F1zgNO0RBuA8/M8pD59LzfDxaOr2JCojvl+Ao9d1fKzx5VopVR4qvGFuVUhc7VRi0S+BaVCixeW2bEx45msOJZHAaoxUqDnHKqZXg55fC1UIJiAOmUk3ghUBe1TS0yb7gjjOCUM6YrgI50yhkYX1XklXvN2u4oo4g/Le4Sm+OO+CrdKYc6VzjGA9LPW9/XJ74qslNOsbfEV63GjYIZQixRjiJ6MyzH9X66zSnH937HJMrBZ/o+ohzlGJ3ZtXBQfBWKciJFfDaiCluuBTJKvE2U09M1K1WKAPd5NuhxEL3P4vgqhmGYucOiHGbxPPOJTaecOx4wxJowDKNiihLSUZxy1ub3rMisp9jggNm6RSDE9EOKZKZ2ylnZnXJQKLZrccoZ07FmztbMsUAxVZ8BPGbecHwVI48B7g+ZEGgzwH2vKedXSmeOMWeaojDm8j3x14XrWx+V9+JKkcDXKWdXLmO17n6vmFiUoxSHwe2YkE45A/ql3DG+qshJwZb7QQlut9k55SQisNLjq/RnDimWq16nfUHf5xNhEQ3EJJhTTlH2/9mq/XykXYjNlY1G05jiqyBr9rlUNGFzIlPiqxzEkQCeTjl9C9eeYsgiV+OpAMpjsi3yPGVhgH7uAYRxirO5HUmRgaWvbIhyejrlmEQ0MkIH/L+rq+tTijT6f497T9zusdwEG4IhXZRDnXI6RNZS9JXwuefDKE45jqLJEOjxVVPfgzAMwzDesCiHWTxf/YWPg3/w1z5H+dstdz0MRcHFQIbpRMZXdYhyqFNOm+hGxleNKcrpiNxi/MHtOVp8VcsxR+3FW51yKqHY3iC+LPJxB36U/PWZDTjFQgiwWiYzy4QFGYy0Q+djgAnAYKecy2W0E957LDW+arWt4lQM7hjOTjkYX9VRvAZQi9Iuyw1OD6ecEDOoscDcJRIQomxPKkKOlMB7doySS9kZAyBdUY4tvoqdclREUY5PKPHIjTdVPx3iq7Bv7HTK0SLEFFGOJdpXOBZ9lRiVFJ1yds11FHl9/M5RlKM45aDwasBzrjW+qlqvra/URTkyPs1XRNMWX2URd7gsb27PBHp/6+WUE1m47BxlmHWPxSwuvmoEp5wx3b1EAaXIk0U5DMMwc4VFOcxB8GNf/4XKv7/9F98B/+Ovv3ei1jDMzOiKr1Kcctriq1pmPcWA46viIEUyY4lyWvafMmja4pTTJsopivIzYz60TjlgnSykiMoDCIcFDk7zfp8Pn3ofwM1vDLc83PfcHzJBQOe1IU45F93dYkIwVXzVWnPK6RNftd7ai29YSGwrCMXGtzgMUAulhghGXeKr9MgjvhbWyPgqdMpJvBAoC5sBiu8hsLoj6Oc7dano2Xabk0dMQjnlYHwVnfThhKWQvT4yF8NxXfS9tEhuezZydcDps2+EqF1Xojvl7JtOOcW+PfI85f4gliinMIhglPU6iiaU4wuaojBXjKIc4n7rK4ru6+aUAtj2PtdyQfoI3886Ld92b0eccrriq5b2vDZGhLaraDLIuji+imEYZu6wKIc5GH7+O54FX/uMx8l/v/qmT0/YGoaZAZht3hUFRZ1yrKKcMZxyUNTB8VVBWa2qGdIjiXLa3JkUpxwAs1POcfn3nSbKGeNhXEcZwFrIoMZQcGYPx1cdHrIAwft9NrztRQCv++Fwy1uaHTozLYOdcq4AbC+NO6iNRc+r99ZikNjrk045JlGOY/EQC6Y2UQ6e2+uJHEQEKQ67FgplIXpAW13iq1JzV0kJFNKjKGc2TjmJFMSk4Kslvkp/XXmtZ9unjOiVTjlD46tyu1OOLvSzLQ/A4pSjuRXpTjldfTF9rWubK/vGcf8OEVl5O+WYRDkzj69SRDkB+nhbPJyrk0kjvgqv+b77OLRTjuOxnCJDruXyHimyKKftHkz+G8/VlvUvzilnhHFAZTLeCE45LMphGIaZNSzKYQ6Gr/7Cx8E//8rPm7oZDDMjsm6XHADNKactvgpnPUUWdJQrqdbJTjnBWVdil5hIUVVL/JiLU05WHbcNp5wJYlMUa2Z+WAaAuojqOoOdWQ5Lm3V3COS7sMVR7AdZpMgEYahTzuXSKWfM+Cpa4Lh6b/z15bvynmq9VUVAsojqONM739XxVfTzOniOTx1fJV0nXJxyqj5ukFMOi3IGgTFER9eUP2MXtAajnT9Tx7HqTjkmQQhAWFFOMaIrgI50yhkYX6U45bRtB4/4KumUc9zulNMQRRDnClu0r3N8VQ8XErotvfenr1NO3hSlFvv6+NWf71MW6cUS5djcdnG/+sZXmSLsXGhzyunrfit6HKOp0HAm6yPKiRVfZXErlKeqRbgs456WIsoZI76KLDu66B7H0zTnNYZhGGY2sCiHOSg+/7GPmLoJDDMfslW7OAJxEuVsul8PiU3UwfRncxRfWHXtYwGOr6tnyurQQVO0bTWxOW6KciZxypnxgFMsREGcDViUc1DQmZXMPCjysAOyLMxiQhLEKedi/5njfcj3tfBgjAirfFcWf1Ybi1OOLb5qr4lyWt6vF4TGFt8qTjmOhcLekS0ELMB0xek0RDl8DyTB7YbnRspFeID0BFbSCael0KsX5k2v+aIIP0b+/sGccory2XK1CnNfgtthc9ThlFPtIykG1J1yuuIBLZFGCF236z2cIsoZ6pRjeb/RKSdvccrJ0r5njCbKsTgXOTvl0LgiIEX8HqIc/fo/xClHER3N7LlQ3uf0ccpB4V4sUY5FMKRcC7riq5bqlBOxL1FEk+yUwzAMw3TDohzmoPnIHQ9O3QSGSRd0HOlitaocVKA9vurSDQBPeS7A458Ztn0mOL4qHuvjdhFMKL7omwB+8Kb2+Cpl0LSaIW9iewFgd1X92xi2tTqKU07CA4qjghnY7JRzcEwhjGPs3PkhgFf9gHlQXORh95cc5J1pf/j+lwM8yPG36aA75Xh+/PxKKQJ2dYsJQbEvBcgA44hyMBJkvVWFDrIwswanWe5FXhaTbQUAWXCaSqxAC5CO4l8U1AxpKxaY9x3OHbZYiUMGhfTH6JSTeCFQj4Oa+n624ZRjcGkBUJ/VhxbzpoyvwuNlqFMOxlfReOTGe7BPATI5pY9TTvX8s63GVvA7NIrkHe21uafI1/Zu76PkPYQ8El+nnH2zaJ3viLsyGW/YXkjbOcsUDRc6vsq0D3vHV6FTjq+zTdH8Xooox7MPnHPEt6+omYL7rU1AORR5PLaJzTXhsq0vm+vzms4oTjnkXIwtLBb4/MOiHIZhmLnCohzm4HjB13y+/P2rX/TWCVvCMKmTlTO9bODgUpsoZ3MM8F2/M5Ioh+OrorE5gujxVas1wMl17a/TQVM5Q97A5qQ5e3ISp5wZDzjFQhTgNYOdWQ5yEJfPhaS45U0A73kpwNX7mq8VedgBzDkfA7tTgN/6XoAPvHzqljDIYKecy6VTzqjxVTuAax9X/n757rjrEqKOr1ptzU45rrPci31VvLZsKyxKxCo42dAL6C6FwiJkfNVp+3t0YcRSil0hkPFVlVPmbJxysPCZiChHFs0dRDlDz9E+EUmhwIkXQ51yChIpZT0fyTOnzV3C5JSDcSM44QndfqiTidUpZ6T4Ku8oouqnazxxsW+e48W+PiapKGdzkrZIL5pTjsUVyTu+CsfIBjjl6P0Lut/2ccqZUtQ3FJszWRfFvnLniiQGl8ejRaAp91tbX7YwZ9MxnHLGjHREkSeLchiGYWYLi3KYg+PJj26JRWEYRsXFKQcAYHOh/NkmyhkT6ZTDopzgrI/jx1fZUAZNOwb8NicA+wSccuiA2lIGNYaiDCKwU85BwfFVaYIDh6aBShE4vsp1Zm+KyFmWiReLDwrNMc+3MH5+BeDoYrziiIliP54op8gBQJT3xOuN+T7EtaDmG1+FMbJTOeW4OjYAhJlBjYVRr/gqvhZKUMyEopzU+1ndfWLqfSmdcqrzzsUpZ+hxmIJTTpcIzgWRV88kHU45Sh9ieQ4uSP9njK/KyuvNals/p8rtn1WL7+izXCd79HLKqUQ5q+1Apxz6b1PbivJ7KM5tonIt0uOrsnJyV8oivViiHCUC27Cc3k45PeM6FVEO7t8B7rdTxt8NRbrd9Imv0u+lAo+FNOKrDI5dALWgvW39c35eMyGdWiMea66iySDrwutJIvcgDMMwjDcsymEOjq96+mcr/z7f8w0MwxjJSDRVF+iUEzvayAUpynFw+GH8uHg9wPG107bB1Slne1LPQESmjq+aozNEFIizAQ8gHBZTnIOMna6CdJGH7bvmPPOSXnuYNMD7AFukSBu7KwDbkeOr8n0Z65qt48dXobBhtSkLrcb4KnRnsJyTGIPlHF/VUhCKDS04uRYKQ8RXYT/qEl81lYtQyqAg4Kh6zsgTLwRSd5MU7mcb8VUGQQhAXZAHGB6jRtcxlVPO0Piqooqv6nLK0fuU8o8t77WJcqr+c3uhdvlpOFd07A/XyR59BA94fdhe6OGi4tHvSocXsu+kw1p1TOJxut6Wf0v5nlGPJQQI5JRjEb1JkYGrU44eX4X3/gXAa54P8JmP2NvTGV/VIw6rbNj8xkgawhePe0/pOoixq5GdchrLp85cXfFVe/Xn3OmagBJsHWTZ0eOriEsV/pthGIaZFQlUUBlmXLIsg3f/yN+W//4ffu1PJ2wNwyTMF/99gC/7Xvv7UnTKSaEtS+MbXwLwd35i2jYogz7aDHnK5kJz9qQcvBpx4Ic+IMecmTMn5ABej1l1zLzB/c3nQlp0zYYURdgBWSlsmdkAPADZTjNs+2LB+wAsbnhcU4QAOL9cOuVgcWSs+Kr1FuDCowCu3ht3XVgUWB9VhU2TKMfVKSd3dMrRi1VzcMqptksIpxyn+KoRRWBzAQUKc3TK6SpsjkWv+KqhopyJ3EDzXf39hsZXiaIUSGZrh+3g4BaL22FzDACiGUGE/efmpBYWNcgd8IcAACAASURBVAReDoIWui5jO3o45RRUlOO7Pz36XVOxH39fa045q0117UpYGGA8twL08fid20SzuI9sAkZdlIMiZPz81XsB3vkLAB/5Xfty9Os6fab37QOpu9fc7qtxf/TZz673Ur3bpt1n6M/cijNXh6B0inGzmIRwRLSug/bPse9htPiqpewnhmGYA4KrlsxB8lnX1O4fb/jQnRO2hGES5ulf7/a+TXU+pSSE4fiq8Fx349QtqGYy4kBQh1PO5hjg/GH1b5M45eTm3w8ZURVROb7q8OD4qjTpmj1Y5GFFVLisOQ4e8vGbJllGaqUe15T8vDzmtzS+agxRzr68Rz26VIqCYq8LoIqv2qrFu4ZTjmXbFfuyYGp1ysF1ThRfpYiNXJ1yquLJkP2Prg9dzh3YFo6vaoLiiuNryp8px9UAGM6f1JxyWgqxinCgh8uDssyJ4md2JJ54qFOOQKecVYfLlSG+yhb5gk7DxR5gRfpC6ZRzUgv4fI4lVwfWPs+fuC03BrdZG1JYBPZ+F68RXU45UpSzbQpKQ/Ca5wM8fAfAN//K8GV1Ct4GPOdS16Wg8VXolIOinmrbXrnHvhx67RrqFkb7rKn7T1+GRFFinxNblGONr7KIAJcWXyXHASPe548Z6SgFcSzyZhiGmSvslMMcLP/9Fz9+6iYwzDLYpuiUw6KcRaLMFutwytleaA4oiilEORPNIk0ZtNtNwe6fGZcpzkHGTtfsQVGEHcCcdXwVinJm2PYlIvSZ+Z6gIOZoxPiqoqgLW0fXxBflYNFTug1Qpxyy/ZyccvZ+8VVTCU98jwshwswIl/FVXU45E2+blMnRKacS5czGKSeV+1kUfFkKsfRZfaibVR83lhDQc2yoU05BnXICxldtKiFOoQkhpVPOBeKU4+G6pDgxdBTL++wbGl/lfZ/Twykn39XbNidRiwD1dl5Hcsq558/tcU2udIly+kw+eeu/BXjvy4jAq8VJpnd8FV7DtRgxW5ymEKozjOK40kOYSKMu5/ZMMCSK0vVeKlTb9G2rC5dBlILE0wfIewTIY3dpopyoTjlkW40RXyUnuUH444hhGIaJDotymIPlW57zROXfD57u4J6Hz6AoBPx/7/tLyAuewc8wTmxOyp80p34q8MGEnXKWyWpNZrpYnHL0wsgYM2R0XGc0HhquM/OZZcGihjTpFOXkHF+FTOG2xrRjKpT6XFN2V8qf24vj2b9TN4CjiyOIckh81XqrFgl8nT6wkCSLcpai9HqgC0dvyCxwF6ccZZuEiK/qcsqZOtorYfCeHeOrbJEsU+PrNDVWe6RTzt78On1WHxqxQ/vLMa/pilPO0PiqvNx/9PmyFY/4KuqUI9dFRTnHPZ1yesRXOTvl0Pgqz/NPvx53nQ/y3BbNQnnDKWdTCnNC9wfFPtz1N3Q03AdeDvDhV2lOMl3xVb6iHHTK0WLErKKcQhV6UMeVPn2gFKm2fL+UGSI+HkuU03qfYXDK+eP/C+DFf4u00VH8NyfGmBg0ZnyV0OKr5nYOMQzDMBxfxRwun/2IE/n7x+96GP6bn/lDAAB43jMeB6/+wKfhXbfeCz/+9V80VfMYZj6gU06WgM5zewHgms8GuP4pU7eEiYEyk7HDKWdzoV2UA9At6AlJ4Th4ekjImT098uddueujANc9sSx6+nLLmwGOHwHwhGcFb9bBw/E/adJlUV7kYfsu6ZQzw0HeEG4aTEBMjige15TzSpRzdGm8+KqCuAGMEl+FopxtWfgq2kQ5Dk4fORaSUADVsq0KUmij6xkLWhy2OTYAqNtkSFQfLqdLJKALI/haWINipm1135a6Uw7oBemp46s0J5yGOwKel1SUM/AcHTOqg6I45QyMryry0jmkyynHJ76q0ylHkPgqk1MOui519Fn4DAWi+3rV5/lTxldd6LE/ezjlAJTn+Zo44bTGVwW+Z8z3zZjrvhhFOQP6+P1Z+Z/ilFOU//3W9wF82T8FuPFL3O9JlbgiqJ0B9Rixy7b4qlyLr6KOKz36QCkeaXECShnZ9pb+tosir9y5YjvltNzXmhyOHroD4MFPk/csUJTTNQEl2DrIssdwykFBKf6bYRiGmRUJVFAZZhqeesM18ncU5AAAvPoD5Q3py97+ydHbxDCzZHOiDtRPyXoL8D9/DODp3zB1S5gYuDrlbE/UmZQA6gDDWA+udGCVZ7CUyP3mMIO9D/tzgF94LsD7frXf51//IwBv/ZmwbWJKWJSTJjhwaOqjRF79F+hcHcM+PBZ8/KbFYKecShCzvUjiqyK7XWBRYL0tY3rQrScWNBJEdxvo5ZTjUEiihcSu90WDxnKBfZ/2cZQwIZ1yXEQ5HF/VID8rnydxdn/sgtZQfCKHxqDhjtBSiA0aX7UEp5wqvmq1bhflKUI/+UfzextOOdqEEBT1bE76OeUUed23djrl9HjmlU45J3GdcpSIl3P1bw1RzropKA1BsQvolGOIhhvSx+e78j96vyAKgLMHAW56BcCtby3/Lu+n+zrl+MZXFS2iHEdhrw6Nr5rbGIm+z72ccnK3e6nBbWuLr9LcBEGUx5AiUJ4omjAm+J1i3neNKWbCOHh5HLH7NMMwzNxgUQ7DtPDXPvf6qZvAMPNge0EdiGCYWGRr8jDd5ZRz0iyMTOFaQx/8h8zAXhJ0ECHGAEKxA9hfVbPRvT6fp18MmityAJnPhaTomj2I+yrUIKZ0ypnhIC8VhDIJMNQppyrIHY0YX4WimNW2FAOFmqnfur4OpxygRVQXUc6uXIatkKTHV411rt9xE8AvPa8u2Ls65eSBik8syhnG/qwUMqxa4pdSY2h0S/D24LGF268wv07jLkLGV83VKUeQArmLMMA2AQm35aYtvqr6PHXK0SP3uvosUbj1rX2K6nh92Fwo2+B1r97TKQf7TeoiB6DGka824Y+vvBIhDD1+AMIL3vKz8j95LT0CJUq22Ff7ptrGzvFVmdpOKcqplnvl7u59Lgq1zwjmlLOd33OhdB6bYXyV7iYoRHVM7evXlhhfNUaM/djbTV4zgO8nGYZhZgiLchimhbffci98+oGr9jcyzKGDTjkME5vVys0pZ3NSCjMokzjlTDSLNGkE9J5V58LQwrkoeF/FAvcJDxylRZcoJ3RkkxhhUDQW0ilnhm1fIrKwAP2ccjC+anupvLcAiL9vpRvAeuT4qqOyQJO3xVdZCmpYtFk7iHIKTZQzVn//qfcC/MUfATz4qeoPDo4NAFqk14D9X7AoZxD7szLyB+NAUhdHmyJApsTVKUcR5QSMr2pbxvkVgFvf1m/5bYR0ysEoGerE2sAgAG3rV3SnsIYop9r2dPKIj8BLCiOy7v6qjwMYOqZsT/w+B+DplGOIeNGvG/QYXUeIr8L1hRDG6nFBAMP6+P15uS+U+Kpc3VY+jhwNpxztfoe6FZ3e370c2b8IQ5/i+dxN46vmdl+tiyB97j0bopzAgk7rfYYhejEngi+AcR1fxmIMp1aT4DAWulPOHCe7MAzDHDgsymGYDr78hX8wdRMYJn0e/0yAG58zdSuYQyBbk4GCDqec7YXywbhtBvRoohzqlMMPywAAin17DLv/oREzRc77KhYc/5MmXbMH5SBmoEFZnA07x3OMj9/EGOiUsyNOOQCaE18kCuJcc3SpFgbFQsZXoVOOHqUCjpEpxOHH1SlnaMHfF2wjfmdZrLA55eA+OR7olIPrdxHlDHQoWSL7Kr5KOr3MRJQTM47VB12Uo1+zjaKcgeIwFzeWD/4mwEufB3D5nn7rMIFOOdtLAZxyKqGM8nzZgktMNy6j1Smn2vbbC/XkEUU04SBoydaVe0zHfVmR+xdrc+qUA573fX2dcvT4qqpvpMfoahO+wI39SwhhrH7uAQzr4/Oz8rimyxV53eZi7+fIoYtyANTjh/a1VzrO0yLXrusD3cLwO8RwQoqNbHuP/SyjQCM5nOj3GY34KrI+Gl8FQIRfdPxqKaIcg+AoNLjsbDVOfBVkJH6X7ycZhmHmBotymIPmm599o/U9V89n9pDAMGPzrH8A8J2/PXUrmEMAZzIq1rsGcDCUWpzTh9WxZmQpBTC+lgAAGQCPZPc/1M2CnXLiMWeXlCVTaDMkKaH3mYyvmuEg7xizLBl39Jn59G8uSKccFOWs4u9bWXjclKKc3eW4sQ1ScLKp7p9anHJWFkESXY7VKaf6jmM75cjzE7+jo1hCRracDGsrdcppWyf+nZ1ymuRntaMTwAyccrTIoan3ZSO+ysEpZ93D5YHi8oxz9hAAiG4HDl/QKefkujBOOSuLUw7dPjZhTsMpR5sQQp1ydqf133HZtmNJ5HWf3XW9EnkpNARwv9+STjkoyonllLNv/p63xVdt7AKkPuQRRDnUNXpIH58bnHJEQQQTe/V8c46vIqWfjBw/ORG2Xb6rYzlC/V76cnvHV83YKafPfQ72ObHjq9rEhvTaReOrAOr7mCncpWOjuP9E+k54Tm0ujOCUI9TreWr76YHbAR74y6lbwTAMkzQsymEOmn/0158if/9nf+upxvf81nvVm4mv/Jk3w5Nf8Oqo7WIYhmEM4OCCoDPyDOAsPyrK0WcrjgE75RgQbgPPvRc/0M1CFPPLtp8L7DSSJl3xVaGFKHMWZsm28/GbBkOdcipRztGl8ufKwSlhKDlxnMH17iK65XTGV9FCs0UkWxDHHdus3GJAsWoIsrhbFRilWMvmlFN9bnM8rK2ysCnaizGNyAvuSyQNp5zEhZtK4TMhUQ7Gf3UVYkM55bg84+B5EbKfw2e7k+u64+JccHLK8YivcnXK2ZzU3wMdDwDsfRbGV9mc3Yp9GQcnl+8A9lsYXzWJU44eX7WpXN5Cx1dVy4suyrFcf37znwC88z/U/873tQBHClyPyusqdfrwcsqhIgzSPl0QBWAR5RS1+4rIm0I/b1EOiSyb2xjJkGu5yLX4qoiiHJN4TxcBgiBOf4ZJGqlfi13pE+nnvQ7s/4/iu/3RSW4A6T1X/86/APjdHwy/3E++HeAv3xN+uQzDMBPAohzmoDnd1Tcv//hvPMX4nkvHa+Xft9xVPryd73kgjWEYZlRW66rgY3HKwQFFRZQzcXxVag/LUyHtdnvkz7tQDCycs1NOPFiUkyadTjmB46bk8mZ4DHTFfDHjM9gppyrGbWl8VeQIGiluWY8jyqEiIL2w6SMqwOWsSXxV2znciK8aKdYHC4vYVjkL3NLX4DbZnISJrwJod+8YMrt+6ezPykLWqioyhZplftMrAT72ujDLojScZ6aOr0IXJgenHBSAhIyvarsu+jqS/PkbAH7/X3W/B/vMk+tUl48+COqUY9kOLvFV0t3EFl91Ujv+0L9bowRd46v2ZreeLvT4Ki+hge6U0/Fe2h7p/kKijACIKGdb7ZtYopyHhy9LjwsCcD+3Pv4mgNveUf8brx35mSpaEbkqoBc+ohxTfBV1yqGinLu7l6M45RAh1tD4qrndVwvtePWOrxpDlLNu6U8MsWO6qNlH9DUXlO8U6XiTosyTEbZbNckt1TjU0/sBTh8Iv9w3/O8Af/Dj4ZfLMAwzASzKYQ6aL3zCdfL3i0cb43tuv++q8e+7PLEbH4ZhmKWDgwtWp5xKlLOj8VUj2NbqKLNI+ZoBACDtdl2KZb2WP9QpR6Q3sLEUOP4nTejs28ZrgYUo8hiY4SAvi8oSI5BTzhTxVestwLYS5YQoCraBBZb1pvwvb4mvshaCSbSIFEC1OeUMKFYNQS8qgUOMCgCJrxoYoUFnRbe5d+jFW74vrMnPaiHDahtulvnbXgTwzheHWRZlqEtErPasbaKcjAgehopyHIqceD66inI+/CqAd/5i93t2AZ1yUOSSrdrPf2N8VZtTTrUt0aWm1SnnQnmMYySzFExY+ixcxspyvSr2lUArc+/XGvFVYzjloCiHCFYB1Pgq3eUtBFHiq7b136SjnOX6sz+tozQB6n2gx1cVuRpfZRI22dpHx0yoM6ASX2UR5VBB6dAIPyW+amJRoy/Y3nXKohx0ACvMr1PXr0I7D31EX3NhjEh5KvLOI2+3hlNOYudQvovjFrS7Ovy6zzAMkwgsymEOmu26PgVOtubT4fpLR8a/Xz5byA0qwzDMXJCDphanHBTl7Imokp1y0kAUZCZlhAEEMVD4IQoWjcRCng8ivcGjQ0YKZQz94tDzqbE8FLbM8BwTHduJGR9huA/wdcrZXCiLmwDlz9j3BjkRt6BTToiiYBs0vkoXOviICrDd1CmnVZRTFZxwu451rst+rGprhv9zja8a6pTjIcqxRYAdIvuzOvJnvQ1X0NqfhS/oA6giF5uQYgx0p5xGfJXmxgJQxSAN6PdEbj+WfeOrdlfUZzcT+HqQ+Kq8jnlp3Q6GiSBt+1t3ylH2g6ivV+jouruq7RuLuEEUlZuUJW6xqFxNTPE1rZ/B+CoU5Xj0hw2nHEdRTrFT/7Y2xVdZXIH6EDS+Cs89Gl/lMPlEiPJ435E27M/rn4KIcgSJr9JFOX3iq7K1ujykM74q15xyNKHfEFHO3J679cgyL1EOEQL6ftanba3xVZqYCqhTjuZcpf8+Z5R+J3Z81cn48VWp7adiH+feq9gPd8hjGIZJBLM1CMMcIFmWwW/80y+HT9x9Gd5z633w8nffBgBqxBUln3rwhWEY5tDAwQVXpxw6WEsHL0cV5eCAR2IPy1MS0+5/sFMOx1dFQxGpkeIEMy1d8VWhnW1kfNUMhe3FwL6FCQyNTujplHN0sf63rcgZAukis62jRc5jxlehCGhbFjuLPel7aRHV1SnHQZQjIhec2tCLSt5OOccBnHKq7WqLr1qtIZpb4FzZn5UiC4CqCB+omLM/jVMYakSApOKUUw3vmpxyqPADYHjbMSJpf7XDKcfTkeT8cl3MW2/N79mdAkAGcHxt+7nmQkHOx8xBvOJyzyqFFIb9QB1xMCJqf2oQTHUJWjziq/B7OTvlYF94Ui/DG1+nnHP1b8b4qm34Z2gpynmo/ttfvgfg/k8CPP0b/JalCzQAHMWu5+XrilPOWf2aFHhV8VXUKUdxMnF0ylHiqzYkvqraB5sLAFd84qsGuoXRPmtuz9247foIbIt9eY5FE+VYrk0mMVWuPQcqTs8zfF4z4RK3OBRc7vYk0n0HXZcAkHHwMP09iE6s75/vWJTDMMxiYKcchiE858nXwzc/+4nwrlvvlX976NR8I5oXLMphGIYZFTm4iAMOLe+jMxAR6m4w5sztdcus1UNFCpUiFaRk3E7PazQ75cSDnaPSpCu+Cv8WPL5qhvt/zi4/S0RxykEbfo/Pn1+pI6QARoqvIhEdR2PEV6HDzaZ20NCLLk7FQ+JiYHXKyWsHjq73hUaPr8ocisMAaiFaiUf1JN8BHF1T/t7m3iELeYkIOVIiP9ecckKJcs7izFhPNb7KxylnsCinqMWFbddF3/gqfN+uwy1nf1q6uWyOa2eRPlDnqlWHeMUnvoqKZgAM8VWaU44uyrG6qxBnny7XPhTl+Djl5OflsteG6C0bilMOdPej9HzE8zxvE+Wg288I8VXv+AWA1/9v/suS5x51onE4t9A9irpI4fFc7Ih7UOUkQ12FlPgqm1MOdfWqMIlyHvE4e3yVFOWIZh/oO9GGRl3O7ZlAFP3Fx/l5uU9jxQ41RDf6tqXOSZVwGY+tXHOuylbLEeWMEWNPndJiH9ON+KoJ7kEe/BTAx15vfi0/jyPMibVchmGYCWBRDnPwfOzHvwZu/omvUf72bV/6JPn72d58Q8Xu9QzDMCPj7JRDZiAi+sDoGIiCFMJmNuAUC5yln63CD0QBBHLK4Qt8FBRRDm/jZOh0ykFnm9DxVTPc/1KgNMO2LxKTU44Hu8uqU05nfEkgaETH2PFV6NyAg9k+ogIpJqoKpPTzOkOKVUPQRTl9nHIA+rc339X71BpflYiQIyX2p3Xkjx61NnS5UeKrDBEgU4KC95WLU04gUY7IATYo4LCIcnziqwDU57fGe66WIrr10TCnHLymYxxU6+Ce5ZlTX+aqS5SDTjk4eeQUFOdI27ORKIjYxuaUs6kmszju33xXOdP0iORRtpHNKYcKSjQRgC7KWW9ql7eQFAZRzu5q93HXBp576JoC4CjKOW22gTpAYFvW28rNhEQLKQIDV1EOdcohYg0U9TziCZb4qjannJ4TbaSr3gguhaFBcVwfYU2+dxM4924bHo9ZS3wVri+DRnxVQY4xgEpcshBRjuJaFju+6niE+CoxvSjn3S8BePl3mF8r9nG2AcdXMQyzIFiUwxw8R5sVbNbqqfDdf+Mp8veznfkGh+OrGIZhRiZbA4DQBoIMYHGFDq5NFV8lnXK4+FKCohyHYlmvxQcQ5bCAKg4sykkTORjb4ZQTTJQTOA5rTAoW5SSFySnHpzB+fqUWUQCMUxiicVK4btdi9dD1SYFwD1GOdNyhhaSWbSVjUyaOr1KOiw6kC1B139i3ryt2AMcWpxxbrMQSKRzdh/bUKWdjd39wZX8WSZSjnz8JiHLQQQXATZSD5+nQ+Cr83YR0JHHs584N7iE61CknP++/7WkETZdTjgK6svV1ytFEOfur2r6xiBsKRyEDCihWHg5w+a7cn/IY6uuUY3m+o8stLKIcFAmF6g+wrTK+ijjV5ef9nJf0cw+gFqb6OuVQkRm6Ra0qUQ4V0NN92ju+ShOyXmtzyhF19GJBXYt79oF0u81ttusQl5JiVwnmsC+JIMqR/Ymhn6D3IeiUg+cX/sTPbI6WMyainDOxRDnV9tteGMHNhUxyA5hG2La7WvZZpnM/30VyyuH4KoZhlgOLchjGwHqVwa0/9Ty4/tIRnO1bRDlze3hgGIaZO3KGNj54thRctpVTzo465YzwMK4jSHxVkQN89PcA3v7z46w7VeRgUaSZxUMjZkQ+zcDGIaBk1PM2ToYuUY4UogR2ypnj/p9z2xeJwSnHpyi0uwKwJU452Sp+YYjGNWwjxlfle4D3vLQu9q239b2ILLrgmx2c62TBNOX4Kpz1j6KclaNTTvXdNpYYHhv5vhZatbl30OLoGM5MKfDivwnwthfZ37c/rUU5oZxyhCiXGzW+CkUICcRXKUVi7TguNOEHABEODBC12CYeyPgqx35uh/FVjk45dB2+4DbC7dZ2bZeXmszwR/296GRjcCyixXL6nEpjrTLL8LwiZOjoq6Q40kNsmp+X+7NN2NXdMPK7zSmHCGxyEtcEQEQ5Wf1vKiAJAf1e1KVmf9rfKYeeewCOTjmV6ObcEF9FX8f4KuoqZHIb6moftkm2jzgt4bZ/xOMArtzTvt+LvBYBKE45PUU58l5hjk451XneR1iT7+I75VDhpb58RUC30gRfS3bK2ZN4x0jXaylmGsMpp6jdkPDfY6OL4ZXXIolyYi2XYRhmAqKJcrIse0mWZZ/JsuyDsdbBMLE53qzgdJeDEAKE9qCRH8A4GsMwTFLgIIPMum5zyiEzEJFJnHJEPWgscoD3/zrAOw5dlCMAXGMl+jDUzYKdcuKhFyeYNCi0mZGU0M42oUU+Y8LxVWkx2CnnsuqUsxrBuUTGSW3ixlfd9g6AV/0AwPtfXq1v23Q/UApqFlFBTtpti2uQkRQ9Yh2GYIqvchH/yviq6r5xiFPO0bXl721uC4qQ40Cccu69FeDeW+zvy89rt6L1NkzRBY+FaPFV2O9Eup/1wckpp3otZHyVzWHKN74KBQr0+U2HOuUAtDtT2ZAiSVenHIeoROmUY3CbaXPKAUEEE5a+WBTltcomVCn2dTucnXJQlGNw+bHR1ykn10UAVdGcxletNmEL3HRZiijnvN31oYtWUY5lO6AAaHe5fp/ilFOdC5uj8tikrkJKfJVl/7Y65WhC1msfDwCiFOa0LYf2GT73EK3LW/u5OaVCoe1zb6ccIsoJ/d11V7SG2FwXtIv62NLjTTcLE+XImNLY8VUnYd29TMiYsgnjq2T/beif80jxVeyUwzDMgojplPNSAPjqiMtnmOgcb1bwwNUdPOV/fQ1864vfDpfP6purPTvlMAzDjEtjsNkmyiGDW7TPHmvwnOav4yy3ORajQyIH9SLNLB4cXyV4H8WC46vSxMUpJ1h8FbrNzHCQtxjYtzCRyIgmZ4hTzpjxVZt63a6xLj5gQfvOm6r1dcVXOQhEpIvB1j4zXHfKGavYRqM9ANyKwwD1PhlSrBGiLBLI+KoWtwWfyLClUOy6XU+Q/ZnmlBPgGoH7Icb1Ri98Tr4vK2EHPnM0IksMbizo9NA7vipXJx6Y8I2vkpE+HaIc6ZRTHS+9nXLwfKzcOlr7Kt0FBrpFiYpTTosop+GUQ+OrugQtNL6qq8/eVxE5Htc1jCND8ZZXX0gL/SuATqccg8uLdGNbaz9jOOWQZelOOQD+x1Nvp5wr9edxvIKuG/vN1bYaQyD36oPjq8jxnp+X+/y6G8t/P3Bb+3JMohw50aaPKGc1X6ccRXzs45Sz1wTOga8dRa5dmwzXAoBa0C5EfR7qIrkliXJopHy0+KoRt5sQar8zRW1KirkMfWZ+HkeYFCsWi2EYZgKiiXKEEG8BgHtjLZ9hxuB4s4bXf+hOAAB4xyfuhdvvqwcI8mLiGVEMwzCHRqbNPGybsbitRDm7NqecsYpE1No9L2fAHbygUxBngxjxVSGccg59H0WCRTlpIoU3hsEzeT6FFuXMcP+zU043H30twMOfGW99g51yrgAc6fFVke8NaAzUalUKc6LEV2kD1kp8lS7KcSgeSqcc1/iqiAWnNoxOOQB2pxyMrxrglIOf8YmvSiHyaAzyXbfrCUBVFCSinPUmTNHFVOgORUNIkUJ8VdZ8TlJeJ4Va/DlEUESfcWxOOS79nBC1SKJLlCOdcipBkK9TjhAAn3iLu1OOcq2RfzS/t+gS5RB3JeqU43MsiargbnMXwX54tXa/32o45Xj0hfr12NkpR3OzkvFVKByrrl0hn81o30KPS2yL9/FExCUIikK7rj/0GEeBDnVZ218F8jSREQAAIABJREFUgKzcJqJod8rpE19Fj/d8V4qxHv3U8t/3tDibSWebShAm93nPfqTIK9cnDzenVJD9bU+nHEX8Rj776Q8AnD4QoG1EeNlwTTPFV+FzoCaSWx/Pb9+0UexrIWfs+Kr1WPFVPd2aQoGiG5P4ptjF2QYFO+UwDLMcYjrlOJFl2fdmWfbuLMvefdddd03dHIZR+MTdqqX4B26/X/6+4/wqhmGYcWnYgbc55VQzEBWnHG224hiIop5FWuTlQ+RSZhz1hdrtxnAsGlr0F8X8ZuzNBRblpElbfBU9h0INynYJgFJH9i3cPzTYnwP8p28H+NNfHnGlugU/eDrlXAbY0vgqi/NACHQ3gKNLceKrlKJiVhWJsXiO556hMNMGFRPZCgBYNB5dlENm/QMMcMrpcV+ChYcjdMqxxVcdiFNOUd1P2YrcuM/wfnm1DVPMkaKcGPFVushl6vgqdMrB5yTt2NKFHwDDj0OR2x2mfOKr9qcgt2Ob2xSAwSnHU0Rxx00Av/x1ALe8qfw3FVR0Pjs4xFdhfJ9J2IL7CKAW5TSccizHkihq0U9nfBU6lq3d77fy81owCuB5n2aIxGlt2775O73GANTbwxS9OBSbU04fkRcKLRF0i3NxyqHtUOKrrhLXOVGfS8Ve24a2+CoinkHocZHvyu38qKcAQAZwz80ty9GdcjRRjm8fOGunnAH3OXie6Z8VAuAlXwXwrl8c2Dah9id623RxsDG+Ch1fjpbzzINOYABxnXKydRXBOUJ8VV9hWCikiEu7xyoqJ63Q915FHme5DMMwEzG5KEcI8WIhxLOFEM++4YYbpm4OwyicE+HNEx55AT706Qfr1/bslMMwDDMqrk4568qym87OVQZGRxTlSCv5guOrANTByyTjq4rlDEClBoty0qQtvoruo9DxVXPsB3Eb8LHb5PzhcrvEEJi0EdwpZ4TCkJx9XBUetxfditW+0AFrLEKsN+prvZxyNubZ3RQ9vmp0pxz87o6OfFhMwCJ5n2Mg10U5C4+vEgLgTT8JcO8nut+H27bL9QSg3l64D9bbsE450eKriLhl6n2pu3UY46s0pxwX4UAXxd7BKccjvoq+x+qUc0KccjxnzV+5p/x5uZoQis4zAC3nv0d8VYGiGYOQRImvQqec03qyAi6/UyBJ46u6nHL2dgcgnbwqWk/hlCNdi3SnHJOgdCCtohx07enrlOMbX0WuEyannN2V2u2IvlbsNdF8H6ecjeqOst6Wx+R1TwS49+Mty8nV7+UTgdm6vLWfm1MqoDiud3yVQZSTn5f7/PTB9s+6tk329abznwjoICv/KZ8Dtfiq9YLiq3T36ijr2BPRZGzhiBZfNcVztR57hhRE3BXy3KaiMR6rYxhmAUwuymGYufBZ1x7Dc//LWjjGTjkMwzAjIwc58UGsY8bi9oI64EUfVsd6kBOatfv+bDmDG31RZvZEELfiw/8QUc4cBQNzIIbIgxlOqygnb75nKLjMOe7/0FFeS0IWlDyLWYMg1w9fp5y8slWnTjnZKn5hSI/oOLomjpAJC503fL7qPgJABss9BCL4GTq7u+0cxkLb6KIcrTggC4U2pxwS0wDQM76qWsZxJcpps9aX23ygGGJMigLgj14EcIWk0l+5F+AP/0+AD/1292dxX3S5ngDUxWZ0XVmFiq+q1hsl6oC4EdhECGOA99arlvMzWnzVUb18E9Ipx6Gfo+/pEuXsrpaOqH2dcrDPPXuo/ImOMgDm818KThyW3XDK0UU5GF+Fjq4Gp5yuYwnfa4v8KfYkvspVlKPHV3nc98n97+KUY4hekteYaj/Q+CrZnkBF7tb4quo46hVfRRwrABxFOVeav9O+andVvZZif1bk6n2oc3wVdfIhTkvo3AIA8OjPBbinTZSjTapRxD49JtpQAe/c7quLAU45xU6L+ER3sEAiUtrPdMZXEaccPYZIccpZwLiVEKA4u8V6BhVVbGAoYXHnusi1AP89NrrTGUL7sZDiJLosdsthGGYBsCiHYRx5/233w3e/9F3y3+f7GQykMQzDLAkcZLA55QCUD960EDCVU44csM6rQuChXztE/wE8p8WzU06yTHEOMnbaIqWU/RXYKWeOg7xD+5Ylgw4HNkeMkAxxysF2olsBQFnMHi2+qiqAHV1Si4KhwAHxv/2vAf7Oj5a/o0BYFl18nHKIw4+tCIXF4MmccrAY4FAcpp+zxfB0getMwSkn3wHcd2u45d37cYDf/z8APvY6so7q+9pm9OP7bKIcLIbjPlgfpRdfdfV+gJd+bb1tbREhY0OFHauNm1POUHGYHtFrQjrlOIhyqFNO1zEjnXKq48XXKUcX5WTE2abz/KfxVW1OOXl5LWkV5WhOObur5aJotFjX/lDiqzraisVhHwc4FOW0uS110tcpZ6f+bW2Kr+rh3NMFruvo2kDxVX2dcsj9Eh77SnzVqeq6hK8VmkuE7X7a6JRD4jrzXX0eX//Uss837b9GfNVQpxxHgVmK6PvcyymnciaSYnLilIOvh2gbQHX+t8VXZXV/03DKqd6zPi77galFp0ORzj9kTC7WelCUGXu7ocPalPFVbU45eSTxjLLcGGJrhmGYcYkmysmy7NcB4E8A4GlZlt2eZdk/jrUuhhmLgtxX3XuFbwQYhmFGpTGTsUuUc0Ed1J0iOqco1MHE/HyexeiQCAA5sB1jsEK6WbBTTnJwfFWaFNrMSCSGu9icI6Bk2x36rbs+BvDwXXHbkxIoLJnEKYcUSn2ccgBqpwUAv+JlX6Qop7qXObroFuviC36/G58D8OzvrtapuQ0Iuv0s7gzSxcBBbIPxLaOLcjRxoRT/esZX9enrZHxV5bzUJhJQZqgbimUhuOk3AP7dl9WCg6HgcmgcrBTlPND9WdwXO5tTTtVv4Pm43tZCsCHgM0CI4tjdfw5w61sBPvmOapm6u0lCopxs3XzWoCIiIC4KLm5SbWBUB2TtfSceK07xVa5OOaeVU05VYPV2yqmuV65OOSYBTts2szrlVPugyynH5jIjhRFdTjm5v1NOESC+yskph2yTQhPlyPiqrP63Hr04FFzXhUeGja/C6zqAmyiH9qmm+Kr9VVX4QeOrfJws20Q5NPIRxVCPfmrZr2PEm76cFf1e9Hq2ctZEK8uTwsCZPXcLUbVdE9bYKHIAEKrrIH42lLOb0p+YRH5kv+E9ku40qIuV5yaa0hGaKCfW90H3p9CReyZQzDmlKEf233p8laGPD0EssQ/DMMxERBPlCCG+TQjxOCHEVghxoxDiP8ZaF8PE4o9+6CvgVf/8bxhfe/4rPzByaxiGYQ4cGV81J6cckl+dn81v4Ck0seOrpJtFz+0sCnYzioUiyjnw8yAl5CCstk+KCKIcLNzMUZwoXX4ctsV/+rYy3uVQkAUlS/E9JIpTjvyj22ex6IHFPoDymtQruqhwj6DS3QBix1fheujveL57OeWgiIkWkrqK0hNY6utOOXQWeBf43YY45WDRYXsRALL2oi4uG7d5jHuNy3eX56HNxcYVUzQdbjObKMfVKQeXvcGotU0gpxyy3qEFHIxWwmI1jQjpehYZC8UpxxRZEim+yib8iBFftb+qOeUMjK+yOeUYXdlakEVZg7BF2UerskC8u6oeSzYXUdq/dt1HFXt/8Z+Mr7JEFJobVv6QTjld6zG4HeS6KIe4PvWJ0+oC13/yyLKPyPflPp7UKac6JvX4KioYk/FVe7PbUFf7sE2yfWv1milFOZ9X/jRFWCmCMC2+yuVaa2rXKqJANSYih16CCOO9FIpy0AkpoFNO27UAABQBnS7GaYhYZvjMRmk4IkY63kTV/4cWEhrXpcXmTTF25eSUE/DYKQzXDoZhmBnD8VUM08GNj7oIX3TjdbBdJzDYwjAMc+hkmiina3BUH4jQZyuOgW7tnu/mP7AxGEEGbSPshyFOHDigwYKROIgJhHGMHekw0TZwC+HOCdGyrjng07ecPhDOqWIOnBsK99HRZvsCeDjloGjlqP7bqmdh6IOvBPi3f8XuBgJQD1SPFV+lfD+cuas55bgU1ArSbqtTTj6RUw6Z9S/pEV/Vp7BB4710QThlaNyHU1schTCuYLFYEbg4OuXgvrCKcqrX0a1ovQ1TzFKERAMLOFhAv3J3+ROjIwD6FaRD4xJZIkVEVJQzoO3oCtPlMtY7vqpFlIOuo4pTjue+RaGZFOVk5Pmya1s4xFdJpxz9eRU08Q3Ujq4N16WOPss1vgpjBFceYtP8vOzj+4hgvJxyqvZsLtR9Z7EvP4vbTRHlBHadwOvZyXXlz93latlVm0OJcmxObTuDU44iyrlSi95ou4qcHKdZT6cccvwU+1qUc/1Ty5/3GkQ58nuuDaKcHtezggjMROF+/5YC1DUI/+1C0SHK0QVqQ9qmXAv0ZzvN4Yge+/okDXlfNPOxKymIj+2Us6/6f+1+OwpCvXZN4pSzV3/Kv+/Mvw+F46sYhlkYLMphGAd+71/+zambwDAMw8hBTpxt3CHK0QciphAEiLwe3BRFFV81w2J0SJRM+kjLpz/7fPbQ91EsFJHHjAZfl05bfFWbqHHQulDYMsNzzMeF69AEmOhw0FZIjUGjCAjgfFHRB+gBugvLXTxwO8Dp/QBnDs4kekTH9mJdjAsJnY2N4DplwYc4G1idcojoZGUT5VTFYN9Yh6HoohzplGM5JvJdVRjEe7UBTjmrTSXKaYuvGugs4NQWjIwKdFyhaGzXQ5Sjx1cJYRY96CKy1Ta8U87Q5eH2vIyiHC0OKiVRjslFxeSUI+Or+jrlVPFVLk45xb79vEAUp5wWIRfuU8Upx1OAZoqvkv3awPgqjO8zClvoMQNl+3WnHFufVTgIofB9q43fdS2vxBmNCTAu6E45DvFV2xP1+FhpznUA1TUntFMOia8CKAVj9BgyiXJ2p+1iiSFOOfgZKX48015vccrBfbo5GRBfVS0DxVgAAI/6nHJ999zc/T1NTjm+D/VU1EfbOQf0fe7rlGOMr6r2fcj4KqNTjnaumoQOi3PK0b5PrGdQ7Hfx3jvmeJI8Bke+16Y4OeVwfBXDMEwbLMphGAdOtmvj39932/0jt4RhGOaA8XbKIQ+oMaJYbND4qiKvBqRbihKHghAgi6gxBhBwoKWP6GOIoIexQ/cJC5/SoW2mm4jQZ0rh2wwHeGXf4tA/6PECS2dSpxyHSBEdU7xT3zghHxGEFOVU9zJHl+LFV1G3BoDaTl+K8DxcW6joxFaEkoW2sZ1ySIERAMpjwsUpZ1e5Q2iibx+oqGTt4pQzMDaosy3VvuqK//Hh3BBN5x1fVbXlHT8P8HPPbr5PxlehU84mTOSB4pQzcHm4HZT4Kkd3kzHAe2uAygXDQZQzOL6qIMKPlmUozh+Wvo465WB/ev8n1W2LYp3NhfJcA7CLfRrrMcRXyQiQgfFVMl7KICSh+wCgFKWYnHK6+iy6/E6nHIdoMZ38vOzD+ggUvZxyKjeJ9VF9bSl2miin6o9Xm+a1ayiFSZRDjiFT/OAvfy3AG/+1eXko0GuIcizPuburABeur35Hpxyy7v1pJZyrjjkqYJJOJkf94qtWRKyF+x2gvCd65JPM8VWiqM+VgohyoKfzGx73veLSHPm9Hwa45c3hl0tdfgD8RTlrw72U3L9D46uE2p802qa5JOYGVxO8F8Zr8tyf2en5AtAhKs/9ryf651fk3jt6fNWqvi5NMdmF9t+UWOIZjq9iGGZhsCiHYRw43phPla//d28buSUMwzAHjBy4qQbUOp1yVu3uOGPGV2VrkLNo9RlIh4icEdpjVp3T8qtl9hlA8nHCYPyZ4hxk7EhRTodTTvD4qhnu/7aYLxOH5pRjiriJjckpxze+akWdZHrGV/mIIPKd6iJzdE1ZjAt9PuRnqgsQQDPqxUcgQp13ZCGp5TxAp4ip46ucnXI0d4g+fZ0SX3XSXiwYQ5RThBblGBwcsHDsGl9V7MttdM/NZSyKXqSRopxKZBHDKSdYfFWbKGdqpxxSiDXFfyntRVeWgU45UiDSEZGU7wCOH1H+fm4RLqIw4ejact/d83GAFz0D4C/+uH4PCry2J3WB1SSi6AKP6XPilON6/ttc2WR8n0HkJ3SnnCq+SnfQ6dofQtTOPtb4Kowa8hHlDBQoulyL0RVnTQQlRd4UyQJUwiJ0eQtU3JXxVSjKeVgTwxiOp3s+DnDfrebldTrldMVXXQG4+OiqDSjK0b4jFX70dsqhUZ+4XOKmhddA5PrPBbjvE4ZlCPXaJW/BMKqrR3yVz7nnS1EAvP3fA3zsdWGXC6AKlPDfTm1yccoJHV9liDIsXyzfR6+NuTZJY+PhlHPPxwFuf3fvZkdFOv9gHFfLsfbmFwL80lcPW89qpPgqRYjb8/llKPle/YlwfBXDMIwTLMphGAeoKOdrvvCxE7aEYRjmgPGx1NZnBxYtAp2Y0BkzIiezoA5Z9IGDeg7Fsj7IeJwB8VVzy7afC1Ocg0w3QhChzIhOOXMUvvk4aRUHJsrZGQr3saFOL2AplOpIkQmNr1r1Oy5xP9sKzvheKgQ6ulj+DB1hle86RDnVd/cRiEiHH0MhyfTeKUU5sgDg6pSjtbdPX6fEVx21nwezdMqpon5MAhdXUQ5+Hs8R/XNYEKduDSEK8HQ/hIqvMopyIonMfVAiS7bm63lwp5x9XVRv6zvzc9WRpAt8/dKjy+P3gdsBQAA89Gl1eQClGAFdHHyvOw2nnJVFiEIEoMi9twC8+CsAHrpTe2vlFLYyPK+anHJ2Bqcca3yVg9gGhS+6a2wX+a4pynnoToAr99o/6+uUg2IbJb6KOLtFja/SRTmW+CohynjKtus0TjbpE191dLEUZ9F7qIxsBypawXYVOXH+OLb3bcq9Ei6XOC2hGAu58EiAs4dblkEcgIZez1DYMkQE1sXZgwAg4ojFsT/tG1+lCJyrc0Xu36FOOSQOzyTe052/TAKKhojF4dx78wsBfvv7ezc7KnpkbVvfed+tpTtb7/VU/T+eT2M45QBMJwxudcohxwvHVzEMw7TCohyGceB4Uz8c/T/f8SXwLc9+ovz33Q+PadXOMAxzwOgDN51OOdqAZdvvMZGW1uty0Kut+H1IiAIganzVgAgqdnKJi7J9ZyjKWCJdQqkYkX9YHJpjH+jatxR5+Z45fse+YLE9lBDAiQBOOY34qgGCDNf4KhrRcXSp/Bk6wio/r2c4I7JIgE45dPtZvjsOfruIbfT4Kln42wH83HMAPvKa8t/3/QXAHR90/kpW5Kz/6vtJpxzb57T4ql5OOaTQtj52E+XEmtmcexyPLuw64qv2V7sFEbRQsz+rBT5XtfhvPb7KJCrpg9LmgcvD7XD57vInLXwm4ZRD2rPemJ1y8BiX4p2BTjm2iCQhymMAxQ+2+KrdFQDIyvfvT2vxFu0fcyKA092/XMHjUImvcjj/qQD00+8H+NSfAnz8jep7pPtHVrmRaPdY9Ll1c6E8h3SBV6dTDo2v6jimRV7HDbZ9pwduB/gPXwnw8GfKf+dnZf9FRTC/8V3w/7P33lGXJGeZ53P9/VxVdVW1N1J3C9EIScihAUmYYTGLhDkI4WbnnGWA2WXYWQYWlt2ZhR2GgWVAIGA1O+wCQiAJNAyCQYhukAQIedMtdUvdUht1S9W+qrrsZ65Ns39EvBFvREZmRubN+5mqeM6pc7+6Jm+ayMi88T7xe/A3/zr/e/QX6vVHyaQLajeclEMUOZJByiHqRFPxVXJ/5MVX2SaO+Vh8d57x1kXK8Tm3ognQWxXGHEXKmQGDdbacLiPlkGmDxaN2hx59Gz82bLncyMpNvL2V7L2cy4CTicCsaExU+21JpBzqP+bLMOVYkWW+215kcCZz6qIEEF9STquFzDgaJ1cBml7nc+6Nzy8njrUJKVMO9SU55+V8vNj+J3Nh032WS7YpZy8m/PH+23ie7UNfk9mHfhM4UZJCEeKrgoKCLjEFU05QkId6HXHD+mXXCvzui246ol572S/+7Z6sU1BQUNBlpwwppyy+iv3otmcr7oZUXnrHHBS6nA0JCt++rPiqBkg5wGVOM1qSgulp/4n3i5nZlEswUdEyD+L55Uvh4pEtl4tU4X43STlWEbCKXKSc2vFV8jj7mnI63JQji2+zbff76yqeOUg5srhChZ9Mka2oiCqNK5wGkEvKsSMp5Psmm8CZh4BT0ojzd78AvP0Hqm1XkVS8HMWbet5nEB1C3V/WaAM8kqI7yI/T4TEidclMvuvSFB3AFU3HiyGTzfzPGqScsT5HJnmmHCLlOEwldbQMUs74vGxrqVkQ2+t7mgwpxzbl8PVlZqKq6x5Ngff/qnhUBrwcagsdQ2V+KOkjZyNhVOytiuKoKqozgwA3wFHBuDIpR66HKpBzUo5jX7j6RlrGYx+13pvovsQ2zuSScqy2VNRnkbkqzwhFMkg5Oe978pPi3zMPiP9HM7FPuUli+xSw80z+96j1qkrK6ZhELJsip0gfzJTSmCnHEV9VFHVH5q08U1lhfFURKWckDDC9NfMeiuLeANPgyk05tNzuoHy/8Gs9ibcL2xDVW83ez2QMOC5TTp34qjJK1QKi/mMppJxExsMtQsohM/kS4qu4ATPTN/O4POvemdqSIuWUkGW4ptu7+xugihLP7ZmPTYNeVZEZsuk+y/1lrJ9syMhcVRlCJbL/923PH3wDcN87it8T4quCgoIuMXXL3xIUFNRqtfDnP/YK3HJczGh8xa3H9niNgoKCgi5D2T9yi0g5hfFVu4SZV3jjjihIuNblslNaf1ad1+KbIuVczsdoSTJoVcGUsy9kmHLsTPg4/7W6Osi0MG9SjjXT9HKQq3C/dC1CynHFV5XEgeSpCinHVfzy/WwVkdGEa5H4Kr68spnhNimH3kfbSG1lfB64+BiwdRLYaCAaOtOnECnHJ76qJwpsQE1jlmUU8I6vWsI9UNOkHBVfxbbJMOVcBNavLF4XQJgPZjmmnNhFymnClFNQaK8qZQxJRdtNrcLmXkeeGqacbrYQZpN96LFqO3z848D7fgm48eXagJcXkUT7fOUK8VjWJuc7ok/sDYXZS5lyOCmHKGd9ZjSsSsqxzBVGf1USX0X7kLblsY+Zb6V9ApSbcrpDEYdGBFH6jsIoQW6EKnlfu1t8XSPqExWho4novzgpZ15Cw9IbJ1efSDklcYjtrugv+f2Si5TT6TG6RdPxVYfF43TbbEP29k6l8TCPBMgn4JB846s2rpWkHNkm45m+LwCs+CrZnyURI5kMy+kkeaYcahe2ibeIlKMiue34KmnESplRoEwZUk7DvwuVKWcJRhEVI2cZa0o/Z5ty2LmiotyaiK9i54/dP/L+hrcJwEHKGZr/L9Jse/8aJdT29M3/24omC5JypClzt+OruoNd/v0lpUg5kft5wL89x9Py/RXiq4KCgi4xBVJOUJCnXnLTFTiyKm7knnVsDf/rt3zpHq9RUFBQ0GWmSqQca8ByLygoaqCubZJyLqdirS01CLek+CpFs6ixjwMpZ7ky9m8w5ewLGQUjm5SzjPiqBUhWey1lKCrZF4GUU6w4aub8d5JyqsZXWUXAWpQUeZzLKBD0Xk4DWGZ8lU3KoWJE5CLl+BRRbVNOzvvJeGS/jwoGtjnnibvKt8dH9vnW8mwT8Vy0g7wIjfkY+MI/lC8D8DfltJd4D6RMOU2RcgriqwBd+HTJiK+alMdXUZvt9MS+WbSfMIxEC/bH/PzeOZM1uez1Nc0oxHYdpBwr6gKoF19Ffed8bFKxnKQc+V5OJCnSbCQMCt0VK76K7Xse/9KWMU6VSTnWepDRACi+vvNrDfVfZx7S5hZAT8YApCnHNqPz+Coi5dhtqYiUQ/FVneJ7jCQqf9/orHiMJuJcS+YyvopRg+bjfPKXsV5sncvMkIriwwwDRM9Ry+DxVfI63VQRlvaHEV/F+je7PRENrEp8lc91dT6WJjRGpqHoSeoLudlHkXJiRjIZlPdtLlNOi7WL2I6vWhXH3BVva5iNLPIbUM3gRwZeMsQ2TsqR15llkXIMM19FUg6/n8qQchqMr1o5CozOW687qGmkxDJtKxOLx/VzurWPSTkUXyWNnEWknDSu3xaTSF6XyEi4TFMOO47d4WKEn7rKJeWw9uLTntNUtJ2yPj6pYfYJCgoK2scKppygoJr6kzsf3+tVCAoKCrq8RIMHRjxBjtpWvrJrcGnZ4rPA+Kyzy6lYaysFxGzTEkR77eUTzaLGsgMpZ7kK8VX7T96knBBf5W0oUoOUl1E/r0g5OTPJuf7w24C//4UGv7wOKYfRFki7FV/FaQDKlNN0fNU8a8pRpBwaIGemJi9SjlzvVkEBLU2BC48Dh6/PziCneyAqbBL94smGTDmZwXxfUo6M5soryt/358BbvhPYLohw4fFVnaL4qgp0orpS5CaPc9FH6tzOI+VYBhsugz4x8YivkgUzRepYsOjCi7FNxVcBmm5ixFftJ1JOL3s+uEw5rnY4vgDsnM3/npi1L1VUz4lIqhxftSOifHorYn8rUg77HDfAAfJ8W5SUw2kdOf1a0TIe/7j+2yDlWIYYg64EsZ3RRLR/FTdTYuRIU8/4KkbKyTO3kZkonup92O2bVNr52NPUYJtki0w5ct06fX39TCy6G2/LqsDdlDlcfieRcmY7ZkHb7r+nDmITl9OU40GEnY9FG+ivMfPjVLRpul63mfEjZvFVivxRN76KGcb49R0Q60TrR6L38uhF+3omvqx4Xez1MqIulxVftQSjiCIh142vYvdTNimnifgqWq+1Y8DojNkOuQnQntwWW79fyMTi83tmtiPa6F5fC12y47jy+hJq83WNURRfRcd30WNZ+F0p1PHr9veGlEP3Vfb9hkG08dgHSQQgLd/vIb4qKCjoElMw5QQF1dRj5/QAQZzsw5vPoKCgoEtNdtGkCJFsz9zM+3uZ4mh3Xqy8nA0farBoSbPEm4qvOoimgf2uJJhy9p0MU471773IAAAgAElEQVR1TJbRZx7o+CpPClecM0h5KYsKlElUPgB7/gRw/tHFv9Mg5agn/T7rjK9q12vnVeOrOq74KstA8dC7F5v1Gs8K4qvkclNrlnshKWfuR8rZPg3MtoBjz8lGMyhTjjQgUZt58pPem1Uo+5rtUxwGRHttF5ByVHRJwfGlNt/pyeLIHppyGo+vkseJt1E7vqpsXejztCz7M4qUIwuATUU/5BmJ6mg+1ufA6IxJPWl5tLNly44ssa+xTlOOg5Rzx08D7/hn+d9DxySaMOJJCSmnSnxVX8ZXzTkpxxVfJY9F0fmWJ1d8VSEpJye+qt0T/SqPsCKjEuAXXzXbAZ76FHD18+VXlExYULE5JXGLZccGEO0YENeamJ2DtP6xfN5n/6bWPioj5XS6ZrSObVjlNCd1bBq6p6I23FsRx2+2bRpxdpWUsyJJOSy+qstNOYw6Z8RXyXbVHZbvl7z4KmrriYOUQ+vnWgaZjYzlVoxxAnRb9qFU1ZEy5SyLlFPDlMMNvIBFypmY71l03QBg9bik1PE+r4CUo85HZvoCPE052+Yy9pPU+SLbeV6fSGN1dY1cSSSvJ7tBymHmqu7Qj2jWtGwTl3qetQGffeBLieLLulR+Yz/2MeA/PAsYndvrNQkKCtoDBVNOUFBNvfYl16u/R7MDWFwICgoKOmjKFE0KTDn2LMI9J+XwGbuX8zUjFYfNZwZ7rcU3ZMoJppHmFUhE+097Rco5iMfft2+xB7WXpdkOcO6Ly/0OXxkkg5KB4XjW0KC9o1Dqe01JLNoCIK7VdY4ZnTc+ZJLc+Cq2/848DPzx9wIP3lF9XUiu+KpWy4wLqWIQiSO9r4qKUOceEY/HbtXvVQUnuX9UfJV8fPLuZs6VzH2VB6mAPtfp5hcGeSE0dxlUaOvKGIF9YMppqhA5d5FyfOOr2D6LpvqY2/FV8VTcJ5NhramCVpyzznU0HwGHbxB/q/gqVtjc83vGVPeDthkEcJtyXPFV26d1rJFLPL6Kky5c50et+Ko1GV81ZqQcB2m0zUk5FYqRSSyWbVPSfGgdPL5qPgIG68B1LwEe+6i5/LbDlJOmMIrhgDBkjM+J7bzl6+lLivssFV/lOMZqHWS0EJly8vpXIuUQrQcQRXgyx9DxapyUIw04nZ4+x/l+A0yDmSJnNfTbmRsj+mvl8VXTLfFoRzqRiIDEDQ4uw5utiEg5q7pvpGu3iq9ipiQVPckoNd1Bed+WR8qhth7PzPsSRcph9yVqGR3d36lt4/dgFfpBFQG1bFLOkkw5nGJUtt2PfkQcJ5v05YyvWtSUw64Fa1eKxx1G+ssYSplUfBWRZTwpVXGk9/N+jLCi9Vfkn5zjRWN1dY8BUcCa7rNcMoy4NcypTUiRcqzfdEZ8lce+5ObMwvddgqScsw8LeuT26b1ek6CgoD1QMOUEBdXUr3z3C9XfD57c2sM1CQoKCrpMxJHaQDVSjj1bcTeUpnpAjQ9uXc4UFhoMWlZ8lZr5V2MfB1LOchVMT/tPRr9otXnjfGiqGHKQ46sS8zH3fbsUX/Xx/w/43X/c3PKmW8Bf/Fi92XrcVFI2MBw1ZMpJ7SIg4E/KaTK+Sg4S2/QFl2wagCp+sc+O5f4nQksdRQ5TDiAKeJFtymnJ+6UyUo4VX+V6/9mHxeNRbsqR53omvmoErBwVZJ0zD/ltV5Hs861FhULP+CpVGLS2a+5hyuH0js4g/xzIRIAsM76qYVIOL2x6k3J4fNVYt3NXfFV3qP+vSDkL9qFRg2Z4bsoZnYMZRbQPTDl8fTpl8VVUkHW0Q144dsnu73ziq4aHALTK46vmIxlfNRT9hTO+ykXKqXA9oWVRsZpvA+C+vueZZHprwJVfClx4jL03sUg5RNhjZDISb/M3f61+vdCUk7D4qpw2p4xLJUQdutbHM7cpZ0qmHI/9W4mUM2fxVYws2M6Jr2qKnKW+nxkO+uvZ+CrbxMGvxa7rPCdW0LFXFJWc/RBHYr/3VkU7mvP4qr4menDDGB0jI75qWH4/7Wp7rbZuJ7EVHeaKr3KR9eznaF+UnedqmYlsowWRmIuIzJ/LMOUkFUg5F58A3vytwAO3s1gohylHxVc1SMpZOy4eudHSvnZxxcwkB/jHV81YLWQ/miUSy2RURsqpS52h/rmpe5jiLzNJOXsRX5VHhuX/97n38iXlXIqmHLXt+9DMFhQUtHQFU05QUE31Om380ncJ1OwVa46Bz6CgoKCgZtWyTDmlpJwcMsduFYRV5njHKg4cwIJ0U1KDQfudlHMZH6NlyYhDCqacfaEiGo5BF2uYlHMQ+0AVX1VGyqFB7SWbckZngfH5cpOQr566B7jnj8woDl9xU0kZMaZxUg6qk3JiNlNeLSOneHn2kWKjUlVSjiu+ihexaFb+fIEBdld8FWDGhRhF1DJSTgVTTrsHHLlJ/J8bnWj/zHfEd8+2gWe/Sjz3xF3em5arjCnH8z6DCpLtnMJg5DFzWsVXyWJq3uC6QcopIFgsIhVf1VCBZuYi5dD52/KPr5ps6mPkiq/qsrEUVdBasEgRTfXvhoVJOWNg5QjQ39DxVZw8s4z72SrixoA2I5AYr9vxVS5TzrSYUJRYphwyDbj6A2WgGQgiSZlRbEbxVavivCPzlhFfZZEmqpJyaFlUrAZMY4DzXsdBZQNk9NCKRUKNdV/SZgQhF62ETDlXPQ9Yv0q+XkaZiZkRKuceg7ahjJSj4qum5rEigxIV2hsn5VC0c9e8X+KGVU4bUtSJhvpLdf3vSlLOtt7GziB7fzJhphxXG/Y9t7io+K9IOTy+ipNyWLwTXy/6u9s3z9eP/ifg310B/PwR4CP/Ua8fXzfadsOU44qv4qQcFhteZMq5+23AG24DNp92bzeXHV/VdB+qSDlLKHZXia+i9jO5mL335NcOZbpqOL4K0FQswCTpZEg5Vn/lG181ZRS0fUnKofgq2p6cvmS+IO0niTTJDNiF+Cp5nLnhfreUxFD9vL2dVWOmYk9K1KUYX3U5Rl4HBQUpBVNOUNACunJd3NiNZwewuBAUFBR00GTHCxSSctrm4Kph0NmlwXMaJG+3rRlnl/E1g/bJsooYTZlyDqJpYL8r7N/9p6L4qrz+cxEpY8sBPP6+0VvJLply1KzakoHYyUXgrjd7mBPkoOj4fPV1mY10gbGogJem4nuaGDw2SDnqSb/P2rQFQM4cd7Tzt34X8IHXFyyrApmEm1sAHV/F7w/IlBN5mHxyvyeHlMML2JkopZK4EdpX7RyiDCAMTEdv1u8xohlku5iNRMEjTYBrv0Ks59nPV9s+5zra56UvKUcWg/MiNDidIHcZdnxVTvvejfgqZRJrmpTD2iO1+ZUr/E05fKa+K76KZuQDwOCQeJwsQIsCRJsbrMvvWLDPme2IYvXaMbEtGVPOXpNyeJRFLztD30VHcMVXRbPi2f20HynaqN0Rv3GKSDmdvth3ZTSx+Ui8j64l26f086SEGeCA6sVIZcq5Sj/HjQFV7k17K1lCQRrrvqTTd/S33NQjt/Pmr2PrUkJd4pM98u5FFCmnm/++NGXxVVNGyunrdqKuRR4FapuUUyQVX9U3Y0v4tZGW0VlCFExim3J29HoMNopJOU2Zcuia3x2KNj/n8VUD3b55RBK/T6B21R2a/ewTnxD98vAQcPJevX6AeVyI4pSmYn+UknIsQymPr+LRXedPiGvCh3/Lvd1cdK4UGuIW0LLjqwxTTsF9Bo91su89l07KOSYeeXwVj9FrWeVAe1IBtcNSUg435ewBsaVManvkfYbrvExif3NI7vfEOppvkeX4iMeQdQe7v98Nak2U/5qPMSny/D17KZJyqM3tRzNbUFDQ0hVMOUFBC2ilL350j+cHsLgQFBQUdNCkEMcepBx7MGwvKB0qc7xjDm4tu1i7r5XqAbxlHAdfmoXzs4GUs1SF+Kr9p6JYP4Oi01QxhOLlDmAfmHj2LTQ4uWzjmS/y+t53AH/1E8DFx/2WV8uUswOsHjOX45KaEdgkKYfFV3mTcirEV43Pm4YCW1VMEElk0nk6PfF/Thqi4sZCpByryKa+j8WFVDGIeJNyHgGOPUf/n5t95iy2igrjgw1ZFG3AQOKKr/KJFVKknJyifOQTX8XoHZ1+fnEkEwGyBGMytW0fcpOP8kg57Z4gxxSZcpIcU46TlMNMOStXyPdZ5p2qiqba4LNwfNVYFM9XjwszgxFZ42H+WrZ4IbbdrUjKYesez4qLaHR9o36qRaQclymHFaD7Hqac2Uj0B2QKoDYzc8RXUX/UKSBTOb9Drvc6N+WwiCBXf+E0gEKsa3co6UKMAkh9SW+o+3GXMaIrt/MWZsopo3ulKYuv8jDl5MVcTTf1cY6neh92BlCRhiq+auLRVzniJPM+o0w5PTPuk1PkeFtumjrBI4TIlEN99vBQ1uTFzYGua9UippyeJEPF0gwXzURfqEw5XWacYPszYqacNNb7eucMcOxLgI1r9X2Fk5TTEctzGZQVKSfPlCNNeOo5tmw6xz/5ZmDrpHvb+TLrGuJ8tFRSTgzDlFO07vxeXZ2bZMph9yjRgoYQtW7clCNj+kZnzNfV+Bnrj1ptNqlAbh+1iyqknP1olqDrU1F8lRHRWTe+iihgnvttEXGjbddB+Fq2kgLjTVXzjK8Z6lI05fgakoKCgi5JBVNOUNACWulJU04g5QQFBQUtX2rgRv7ILZqNZw9Y0gADsHuGC45mDvFVQmkKPQgUSDmXlQxTzh4XsIKECkk5lkntzMPAb7ygfKC9SJdFfBWb+b1M+c6qpeNVVpiga1RVU06aiuLPylFzOS6pdW6gSMILpUX3AnEE3P7TwNOfMZ8DTFOOTdcjRZNiww0dZx9jiU0DACRBgsdXsUJoXdFse1tdRiawZ7kXtWs+kz7PlJMkwLkvAEdv0c/xoiQVp2fbuljYWwX66+XFeh+5zreWR3xVMm+QlNMTRdJkrgvhFx7Tf1fZ53VF/UETs6YjMmi0rILVXBSBhoerk3La3azZJmPKOSIe6xgEjeVORPuy16WOlCnnmIyvsmgDe200zpByXKYcMhHReney7TCelkS1yf6D+qmiiCRufuyvF/ejdB3prWpTDol/jtN3AEkIqGLKkcuiYjWgTS5Aye9DV3yVRYjjpJzeKiNMMUMe6YaXAs96FfDsr2FfUdKW1O/KrmnGsN8DaAqJa5t4nI1NygHE8omUg7T8fsZFyrHXbUf2AdyUk0vKIVNOz98Y4KuYGSPo+kOF0cGh7P1JFVKOit1yUKi45lZ8FSDafzwVbVvFV7G2yaWOlxUvNDorotn4fUWuKYetR9tFyuHxVUl2u2yjDn2GzL8feaN720lkYMu79i4qus40ZVDlUpOuWvr/eVKknAnrv5jJ2TblNBlf1V8T5r9MfJX82+jPVvW5Qf2Yr2GKou6A/Un8oPUviq+aW/c4tb4nsoyEyzTl8Gv+LpByRufEOADJMMgUxVd57IPI8/fsJRlf1RAhKygo6EAqmHKCghYQkXJGswM44zcoKCjooKllmXIKSTnWzM001oNcu0nKIdRziK8S4sjnZRgz7OJXFfH12esCy6UoI0LuMj4H9pMMU451TIz4qgg48xBw8THg3Bfrf99Bjq9SlJ99Fl9VNgC+LU05ZQN+dUk5FEW0KukWXqacJgYfHaQcl9Hz3CPAnb8LvO21guTC14MXu2j2N1eSiPcWUWuqxFfZNABAFOT4Z1VkyKKkHFd8VZ+1F25q8iHlsJndQPb9m0+IgqJBymEFf7oHmo10sbAvKQHzJZhyFLGhLLZN0otySTlyvQuNCoyUQ0XteCaiPH7zBcCf/ZDYfk7LOAjxVXRcVq4Qy6XiSjwT21rJlHNOPG5c54ivmrlJOfb7qiqaNhNfpQwjK6LgPToHIDVNLnt9z2gYA3rZ88GX5hHPi4vC9BoZ6aio7iTlsPOiLL6KriP9VU2QIc0dpBwyanSH1YrutA55pJxK8VVsXRVRK9F9CV83lzHi+pcC/+x23UaBcuoSj6/iy+WibSgi6nByFTflkJmz3akYSeMg5fDtOPsI8PpbgSc+qSNe2h6mnE5PP+9T3C3S43eK40FGw3ZbknK2xbWrJf9v31NNN/U2udqw89wiUlvOsaQ23VvRZJrZSBSnO33dH3LTChftN3ofnWs7Z4RxsL+mv8PV9miZdFz5/YIPKSdNYBjNOCln7UrRtp/+tHvb1TLTasYPADjxIeDJT5a/D9DXpmTe/EQAPtGr9P6J3atzA6/92UWjk0i8PQLimrVzxv06fx8ZigFtmPI1l/DzYj8SP+j4F8VX8YjOusaiRO5bus9fqtGC3YN0h/XX+c43AR//nfL3feD1wFu+U/+/aFJP1fgq1fbL4qvY9+zHdlZHatv3oZktKCho6QqmnKCgBTSRsVXv+OQTe7wmQUFBQZeBFiLlRHtgymEzjQzM6wEsSDemVJMNlhJfFUg5+1Yhvmr/qSiiKrHOB2WoqDkQxosTB/H8ovUvMxPSYGRTUQt5ijwHMbdP+72vLimHCj8qvsrDlNPETFoXKcd1bMgMsHMG+KPXiUHdWBa++D2EK76KtqWo6JtUMOXwGChSzzLl0IzjRWZ3k2nCVt34qiQqJ+WclTNoj92qnzNmgZO5ZarJA/11GWuzoIEkYQVC/eWepBxplMorcvvMXqfj2mrpok800YXvz/5X4I++RxsXfPZ5XTUZX0WFNjq3eeGk0y835ajid1fvi0PXyugctu3RxCQ7DRsk5Qw25LosUNCPZ+JY9VaA1aOiL+FF2bLIod2QMWu+65i5HmcLsS6aRzQtNj/QclV8VTs/IqlKfJUyKaxp+gwg2hjvH3g0EyAMLVVIW7TeuaScsvgqiyzRZec7kCXlFJlyXCrrF4jQ0S6IzeH7KM8wxYv08ZSZPIb6szySpuyaXUbK2ToJIBXmzSQS29DpM6OfFe1I69EdNkOdGJ0Dfv+bRZwnv57x+KrOwE1emmwKYwNQTsohwyVQfCypvfRWxDrQsuOZMHby662rzdgmqiQS5+DorGjbtF20frQsEu1Tek+nhJST2Neu1FouMy31VsS/snvONIaKSqP/l+lv/jXwvl8ufx9gXpuaprfYxhYvUg6jkCkSkoOUk8bu/rTOugHSSHqGvwFwxVf1Vs37Q4P4UnJsqvQVeyEeWQfkkHLYPVPd37nUt6n4qiX+DrTjq+ru9/v+DLj3v5S/b3we2HpKt81CUk6U/5pLvr9n1XXKo385KGo01jkoKOigKZhygoIW0C3HxcyWF1x/ZI/XJCgoKOgyEA3cqAHjMlIOLyon/oMLTSjlM8is9TyIBemmpAaLPGaw11q+Z8SMS/y4BNNI8wqmnP0nPnBmH5PUOh/ovbWx3rH774MiX8oP7adlb6OvSYriq8oKWoQPr1oIp6KOiq8qGBiOGpoJDMCblDOWppyv+H4RrzQ+5ybJuIqXqphSZMohMomHCSKxCo/AEuOrckg5ZKyoYsrhZqJcU46kEBmkHEYf4rShnWfEY934qqfuAd75L3VxIC+6quw+I01Fexhs6Pu0DCln4n6eK2EkIUUumOn2fv1LgRMf1G1kqaYcMoktYMq578+BP/x2ZsqR5zYdQzp/Skk50hzWHTJTznViu424C4uUMzgEoJWNuaqqaNpMfBUVp/trwPrV4hwan7eKsvvIlNPuZYuB3qScWXEh0TbltLvIjUji8VXdleI+jdoa0bNIG9eZJC27qF21/6D3clMON1IUXret+Ko+i9qi8y2JtWGmt1LdlFNm8EposkeBUYWbcnJJObJI3+qI8y8TX9UxY5sWJeUog+uExVd1LVIOI8Lc9m3A978dOHw929YFzuHxeXEMxudN8huPr+r23cSJ6SawcY3422UgtaPsuEEnN76KTGisvc9HMr5qoNs3N4xxxTNJ5GDRXpML4jyk+KoiUg4ZgaiPdZpy8kg5crvy4qu6K3I/lrQZRWOhCVce18PppqcBOhJ9lE+sah0R5QfwMOVM9aMyhzjiq/i9/CJt3TblrNqknNRso6TeCptUEJX3M1yzg2LKocl5SzLlpLGO5gOWS8rhx3kRUw4npRW+byK+k64LxmRDazuNtuxhpvSNfaZ77W5/uft2N6X6h2DKCQq6HBVMOUFBC4jiq7qdgsJwUFBQUFAzqkTKsaIodju+yhissgbUlh1rsp+VAmJge0lFjKZIOQcxXme/Kz3gpoxLUTSo5YzusSg6ygRSc+DvoJ9fiafhjw9qL6I/+xHgoXcXfI+nKWf7lLleeapLyrEL94WkHJoRuJukHGkGOHKTXj8XSabVzhaFaLC0yOBAJmGfwrBdeASy8VVU3FiIlJMTX9UdsJnQVvREISlnXk7K2XxK3OtsXKuf4+Zovo1Eb+qv1YuveuTvgbvfqs09znONSDkFy9l6WizjmhcyUoZtyvEwksWMvMDJGXQuUNuz6SJLja9aoP08+Ungix8QxxTIUrDo/BkcKjHlSEKlYcq5XjzyaKpoYppy2m1g5UgD8VWclLNAAWfGomaofW8+6Vd83y2ljH7Q6WVpN2nCisgsdstlyils6/J6w+Or8owf3EBTVjTkJoUuI+Ucula06ci63hmkE1YQLlNefFXe+S+edC+Lr6sy70WMlFMSX+WSDynHiK8qIuV0kJmgQqIi/ca18ppokVdadnxVGSlHbQDz5HBTDhUex8yU04cR98kpcv1V4LZXy+1ogJQzZQQ6/l39dXH9icbiWHLjKmmyCaxLU47rWpUm2XOK/s415ch20R2KbaV1TBNxrihTDqO4cUUTbboCxLlGx3T1uEmgc7W94WHxSCRBZ3wVu2ar+wVG11Lbxu7BZiPRT3b75UVmHmcN+P0umO34XdvourR+tXhsnJQTm8fcx5QTM1KOK76Kr+MihgPeHoGc+CoXKceOr2pXIOUwk+1+jOGhtkWmQ9f28N8utUk5tmlyib91ef/aHdQ3nsUzv+1VEceyzzCipBzxVZ0BgFbDpBz5W6TTv3TIMotSiIOCgg60giknKGgBDbriFHr9ux8EAPz8X34W9z+9WfSRoKCgoKC6UjMZPQbm7FnvSawHcXdj8Jx+iLfb2SLcfilI3/uOxWMjqkoNwi2piEH7vc5ASIivWq4CKWf/Sc0eHGT7Vbv/XDi+ivrE7sE0JlKbLesbkgZMOWkq+ucTH8x/DxU8igofSVwhvooGXKvGV1kRN/MiU06TMwI9STlUdKJiejSVJhPLtLJwfJVHoSieZ81AdnwVFTcWJuW44qt6LCKBx42UkXIY4Scv5mm2DQwPmcUgo+DkIOX018pjbZzrY1GdnKQcDyLfU/eIx2tflL9dvNiep4SRhFSRntEnVq4Qj7SdqrC5hPsMKoAU0Z3KROv9jBjfcJty+iJmKhrnFzxpv3SH+lgduk48cjMP0SG4hkcaiK/ipJwF+hw6t3ur2lS0dRKq32ktifxYRZx+0O6WkHLketvxVWkqj2Waf51TpBzelnMikriBpldAynnTtwB/8S/E3/01TeoAdL9NfaQynHDSSRVTDsVXMVMOmVcA93YXxldZppw01r/5eqv6POQmyCIVtaU0Fa8ZdBHX+srjqUg5jr5rdFas3/CwSfXqMjMI/1ypqaEKKSfWppw0Ef/nfaitosgZX9FxJ1MQkUqIGDM+L+OrHIQXTspxXeeNc6tTzZTTWxWRbbQOgC460/LarM105bkRTeVrLCaH6Edrx8QyVXyVo+2RWZHujzjBr9MX73WSclr6Xsmm5wBiP/c8STk84hvwO77TbT+DDRGAyHzXOCkn0etdKb7KMhXyCUrLIuVQfJW657PITiQjvso+NgedlCPbli8pp+42UN/WBN2rTEZk5UB8V53Ys3juT8oBgBHddxeQchJpyO70/O69fMcXDFPOASHlvOfngI/+J/H3fAKcvNd8PZhygoIuawVTTlDQAmqxgcdTmxP8wUdO4Ft/q2AAPSgoKCiovmxTTiEpx5q5SUhZIJByAOD8o8Cf/TDw4B27/MUpNCo+kHIuKwVTzv4T9ZHdQbbN2/2nopwsGF9FhZgmSVlPfgp486uLDSGLyjcarwlSThIDSP0MLkUDeaOzer3LBocVKacinYKMnXbh3vkdnrMhfeRLyhmfEwPWnOTD4ytILpOEDylHkUk8DK5e8VVsRn8dpak0ObjiqwZ63/MiWxm5LpmbcQv883y9qdBH4sUqvj1GfNVadXOwPWM3L76qVRIF89TdYh2veUF+YZDOwcJIH2aCov0eTwtMOcuMr5LHdxFSDp3DZ8iUY0XTUWGEaAuTnAlJZN7pDaHu95Qph5NyrPgqQOyzReKr0lSScsiUs0B/zCkuh6RJJI39iu+7JU4/6PSy12k7YgcwqReAPI+oQJzT3uk8mLL4qlxSDo+vypnJP58Aj39M0JmALCnHNuXEM9Oo0F+XphLPa8p8BKCl2zTgQcrJUW9Ftm2wfoIRiboNk3JoH5eRGAxSjoOCCAhyxupxTTCic5vMcfZkklKDBTd5Oq7HPAqSSDW0DfHcnDhjiwyWixT7qb3Ores/mXJG58S+sAkv0UysO7VDl4HUjoYzYo1yrj/KlLOir5t0XbTjqwzjhGxv8Uy8xuOrbFIOGaZdbU+ZciTBjO/7VkuahfPiq+S9kk3bA8T51VuRVD4PUg43xJWde3EkTaAVSDlkplqGKcc3vtAVX+Uk5XBSS8PxVdGEmbQSmIZSqe5Qr5+KFvOkVE2ZKWeZ5oJ4Dtzz9urmEz4BBXD3iUZ8Vc39n0rC0F7EVwH1KEWVSTnSlMO3zd7OWP5maPf8fgv7RhtTfJXrHme/6sE7gM+/R/z96bcDv/P15j1zk7+Lg4KCDpyCKScoqCH94u33G/+/78mLeGZrHzrFg4KCgg6qMkWTAlOOXWBL9jC+yh7crDOTpWnxQaLdFMddL+M4+BbOnZ/lpJx9cIwuNRkmj7B/94VosIwPxpKM/jNiBD5eEIsAACAASURBVIYF46uamPVs64k7gUc/DGyfbG6ZtlR8VVnhgGaaJvX7ESp8FhUffOLEttj+KCtK0/KmF6sVsKlYumIV7p3f0WB8lTcp56wowPIZ5rnxVbYpZ2w+uhQzU06Z0cyO6ADy46vqFpFU8cFlymGzVjNFtiJSztwsIgHZtj3dAvouU47cJy5TTn9NzOivGl/lQ8pR7aLgmDx9D3DlbeIY5BUGFSmn4LynmCbAiq+yTTksvmpZEZ60L+JZ/T6WtjlDyhnrZXf6gowECJKESxTr1WXkE5/4KkDGVy1Aykki0aZ7K+LYLjJjfe6IrwJY4bPE/LUb4gW6dk9sL18nO2KHHvm570NqUPFVZMopIuXw+KoccsaOJLl95Y8AL/th4IaXmaQcMkGRcc+mjZHpypeWM9sRRp52R5t/ykg5vA9p2aQcuq6Q+cYm5UxEX+l9z1sQuWcbKen7bKnCvzRMud4zOiuIKmSWouPK9wlXo6QcFl8FyL7KEe1IarUkEanidYIrQ8phpCWAmXKG5v0JmWTXjos24jLfZkw5VUg5K9o4cuFx8djta2JRi7VNwEHKIWNTxEg5x8V1lWLfeLshZUw51v1Cb8WKryJDGOszXKac2Y74bMcjTidJzDGSsuvVjBmrypSJr2rYlJNwU2ZJFGXMxlviudxmR/QhN4M1acpZOy4e6b6LGzRt8hcn5fDoNB9SDi1zmeNKX3g/8Bc/Cjx9d7XPqUkhBcRsfp9f9zeKiq/aBVMOTXIDGLGtpimnCilHmeELSDnxTJpnun77wGeSCaCv/21PAs9+0GykryPbp8S5xK9li46tBAUFHWgFU05QUEN616dF7vrhFXET9m1v/BC+9bc+sJerFBQUFHRpyS6aFJFy7EGSXTflsBmN+5GUo2ZD7fK60GBQ0bFbdPn8sdJnAylnqbILREF7L2XK6WeL7IllUmsqvqoIH15XNMC1CB2iTKpvKWm7fHCy7jbSPi4apKMB/KIBT4qu4svMXR4rWvBomTLRvl+9IrscWzTwSpEVi8iXlDM6LwxDPRYzQqYCLmd8lQ8ph5mwyvYxj80g5cVX1W3LdjwCV9dFyvGIk+RFzFxSzmYxKSca689uLxhfpYoDlimHEzZaLenJKYiCeeoeEV0FsMJgThsoOs+M+Coy5cz0epIpZ8pNOUsyJlPRD6jfhpQp5wHxqEw5rHDS6euCNrXZzLrMZHyV3Cetti6SGvFVjvNx5Yrq1C7XNnQGi8+q5qSc7kDvjyZJOSfvAz76/9T/vBFl4TC+2sYBQJpReFGYXW/y9heZEHmRvt1xG1B5X0REFltUKH7ONwLf9gYzvoq3FzLuJZHZVoh04tuHzLb1Z3qr5jYA1eKr+qvsfJ/qz9P+pe2IJqaBoUhFbUlFgJZE/iiarPz96XrPSJJyOn13fJX9u7WsSF1KyiFjwliaPLsm5SWeZw2rXIN1YJbTz/hoZpNy5Pap+Cppyun0zXY6lf3U4FA+1a2WKYcZ/VaOim2/8Kh4ziDlWBN7FBFjLl9jMTk70mCzeky0TUCcN5yMQioi5dB6FZJyeHwVM4nNdjTtqqzITBFJvqQcOse9SDkUX0WmnIYL3ikjYpVdy9V1c6pJHyTDFMnWsdH4qivFIx3rlJk5DJPhiv5eIn55k3K29H3OMs0S1AdUvWdU94hFpBz226VuxK4yHEpDUyMTEHJkkHJkf1HXlFOFlEORd3zyhj2Rg67TZBAuXTb7PVs0fmfEVx0UU86ONq4TIYe3i9jjN0ZQUNAlq2DKCQpaUD/7mi8z/n9xPEcqbybObM9wYXRAbhiCgoKC9rsy2dZl8VWWyaJTgPtuWgYpx7rd2g+Gj70y5RjxVWh+drGiWdQh5bB12Y02crkpxFftP1Uh5dDAWm2stzy/qNjQ5DmmTDkVY3CqiO+PskFDUt3+VdFXPEg5RYOwnBzkG18FVCNUZEg5RaYcHgux6GC1i5Tj0PicJOVwU848awJw0R5oW4qoI/x4lxUKXIVHu9A3XZCUwyNjbHV4NAc3NfmQcrr6/YDDlOOKr2LFqvlYt5GdZzQpoc9m9PvKLg64TDllpJzNpwSl4zppyskjTyhSDjuX5xMRoUD9AKd3dIpIORRf5bHP6yqeiQIysIAph/YvK/ICLIJGkqboeOdRShJ5npE5obcmCDiAFV81sY4dgOGRxeKrlMlgKAs4i5hy5H6kQveGjOBq0pRz758C7/431aPcSAYphxXqXa/nknL4tSuvv7PO03YXzug//t5OXxA+uDmFRAa9tav0c9QWyAgBMFLOzOxDyRjmbcrZ0cukx1a7XvxHb1W37bmLlMNe846vKuiznPFVjnsM+u1LpByker8//LfA7T8lIoxXj2mCEW23iq+yrlONknJi05SjSDk58VWAOM5TTxqSS1OLlJOJrzortt02k1ARdXhIGmhL4qvabNIJnVtbp4ATHzKXS/ujOxSfWb9GHBNAnC/KlMNoJYBuU/E0a2wanQEGh0VbJsPZbJQ1aQD6GpFryrHMwtR+KU7LNuXwY94dyhiwMlNOYpqOysiS+4mUUyW+UF03pyZVz/5sNNXLXCRu0RVfBeh4M/AoQ8uUE7OxIePYeBimfCJsF9V8Yj76itPD0HJfrwxSzgKTT3j/v9RIZW7KYb9vqsrblGOb4YtIOTK+ytcQra67aXFbO2jxVWkq+i0yrpPJ0zBAe1Bvg4KCLlkFU05Q0IIa9rKo1bd89FH194t+4b27uTpBQUFBl65sjG4hKWcfxVfta1LOLptP1CBCTmGvieUD9YxPgZSzXKUx1HEPpqf9IYX0HmTbPC8ApfHi0UM2PrzJfnA3SDlJ7P478z4+e7DmoKEy3HgYXIoGU434qjJTDjuuVUw5tO8HG2KwtGidDTx/hUHv0Tngff+XRX6Qj6WkHIqvYkQDOwIFYIUmx+x+IL9tJZEeEC9rf67CY29FzmaX31ul8OQSHedujimnTnxVwvYXFeC8TDnsPmw+YTEKp4VBo9USj0C1CKu8+KoMKacgVujpe8RjhpRjm3KIgMHOn8+/R0YofFp/Px1XWofYQcqZ7UCRJJZhykliAKmOlaprUrTbcR4pZ2CRck7ep4vYgD7PaJ/0V0XEWattxVfNcuKrLtQ3bqui90AU4haKr6KoGVnopkglgzawoMGc9u2Fx+p93kXKiauacjxIDfbzRG0pIrZ0+lmiDIniq9av1M/Rfh4e1n/PeXwVJ+XUia9aNb+n3dGxQM5+10GBoc/bxVCKLwEYKaeiKSeXlOP4XVkYX8XNO/J9n/wD4M7fE2bVwzdI8wQj5VDbyZhycq5HzzwI/OF3WIbDElIORVXxiBcy6uRpsO5/jF1S19WxeT2j9pNEMjZK3guTOYEIB4MNcTwrkXLk9efv/h3wB68BfvUW4MG/lusxEm2O9tXGNfrc7zJTTqtjthkeU2PEV82F2XXtmLld85Fo07bpkV6na6jzvqSIlJNCG7Ha5jr2VqTRf15stCGqlGovJb8LubGq7LpAppyNZZFyUvibclh8VTI3aYl2fCBFgC5CAcmQcmSbUPFVnJxk9WeK/BijEilntp29T1iGVKRsxftjZSrr5l+veHtfNL4KyI9sbEppCnX8+H1nVcVzcczKzik6rhRfxe8v7N+XZJ5p9/x+6/v+NjxopJxoKs6liUXKMQyaC1KIg4KCDrSCKScoaEG9854nM8/927/87B6sSVBQUNAlLiLOqB/TZaQcXsCL9aDTbphy1ExFhhlXr+0DQ4Iiyuy2KUcOIiyLlEPbU4uUY8WdBTWrNCnOcw/afXGkt93medxUEjcQX0V9IrWBJkk5rOCyLBmknBKqCKmu8YgGxgvjq1iBPE/bp9zr5VxeTVIOFeModqRonWPH7EAfff69wPt/BTh9P3vSRcpxmXLOCUILN87Es6wph67T/JrE21Pe4HoSieIxUG6CILQ9V28VKvoqSXRb9olocKmMlMMjxABPU45lJnJFfU23s6Yc/r5orGdsj8/rwjg9VokjsIsD1Hf1LFJOq41cs8TJe8V7rnmBfHtOkVvRkti5TIXaraflazNdaOMxArSvlSlnq1m6ii06xweHzXWvKn4OtxkRR+0LaYygAuJ0WxyD3/tG4BO/Y64PRRcBoo9otwWlgcdXRROHKecKcSzyorFc+tw7gSc/ZW5Dd7j4rGpqm2SyOESkHIuIsYiorVCETVUZtA6H8TVhkT1GfBUnNfDCWF58lfU8xc/kkXIoGipvJj8ViteYKac7ANASxiynKYf1RYqkU8WUIw0J1Pe0OoxqU9CH2/FVvZViUk63BikHBUZCI76qgPzKqRA2AWyyCVz3EuCH3wu88l+J4xJPxfnSGeg2bbeVvOv6Yx8Fvvh+q92WkXIo4kX2lfHMbJ8uLUzKYbGQcZSNrwIk4cUiJtHniNrkah+2QYPvuzQR17tD1wv6151vEq9tn9bUMECYcohsaMRXWWMIyug1NY1NSSRIKHSN5dfVyUXzuwBpQBoyUo51v9BbNe9/En6/0LJIObYpZzW7H12y46tKaSzs+JcZPyYXxXLJKNL0bwOi/AAVTDkUnZYTXxVNdXtcKL4qNY8HtYmRJOUkrP0bJsOh7t+T2OpnyuKrtncnvopMk1XvbbhR0Y63V8vmppya+z/h/f8STTlkilNGXLrvrPF90VQsq+wY07JtQmVnkP2sMs9UJeWguP3Qb5FF6Ye7JbpezHdE26DrSewwIdWNTAsKCjrQCqacoKAF9fzrD5e+J4pD8SkoKChoYVUi5ViDxAmLr9oNI0oRKWc/GBL2Kr6KCgct9v+ml193uYGUs1yliR7g2w/nQJBlyrEz4eU50O2L1xQpp258FTP58OU3ITXwtcz4Ks/+gQ+21d1G2seF8VV0PEpIOVSgL42vYrE34/PAxSeBU58tL37MRwBauqBVSPfhMysrDECSCcKIU+BxGdZzpEQWxFaPOUg5jvgqwDy2Biknp23FcxYX5GHK6TjiqwBRPONFp9qknAJTTpcNZFcx5cSRWQh3vT+XlMPiq1aP6tdUhAyRLiqcu76kHLvA/fgnRGwVICgsgw1dvHSRcpJYnze8f6R1JdMbL7R1WDujOAoV87TDirc5s7UXEa0rfV/d/pCfw31WYKU2SaY2IuXMtsTxj8amEZBmS/P4KkAUiCmaKonFOdexTDlDWUSuYhC8/aeAj/223AYy5QzEOjQRX9XLia9ykaOqigoyjZByGD1DLX+qz49cUg6/duX8NrD3YxF5gIhKQD4pZ/sZYe6iNgKIc7c7FGZHO74qsUw5itZUxZQjl0ntscg0BOSbZPpr5nYp44IdXzWqQMop6Iv58otiZQxSjvW+6aa4Ht74ckHU6gzkNXFm9p/0OTKc5t0H0H4nChjgScrpskjpSF4bC+KrBhvNkHIiMgVZ8VWAjI2y2mkmviqPlMMMegYpJxH9x6HrgC/5RuDxj4tjceJDwI3/SC9j4xr9d7evzZ15pJx4arWDSBhsiEbHzWyTC/o4cg0O6QJ7Jr5qxR1fRWMahaacoRnjmCfqs5Qh2jO+Cig3LY/lNnOyUFXtnNHxeraI8gNUI+XYpsI8U87C8VXsvri/Jr5nyiYvUPuw21YyF+dtKslV3qQcef/X8YktS+tvX11SjhH9l2MijSYAWmKb69J+jPiq4fImiqSMUgXUb+dp6jcJBND3KDYpp7eSvS8wTDk+0Vic0lfQNuje03e5ey3eZ0239G9ZZ3zVAdieoKCgxhVMOUFBC+qHXnlz6XtG81DcCwoKClpYbcuUU5WUo+KrGqazuMSz1jOknP0UX7Xb65Jas00bPhaKAFTHlMOLccE00rgMU064L9oX4qYc+5zhJpo00YNudQcrucmH/78J7XZ8VRlVxPV3FXnFV7GB/jxtnwYOXy/f70HKocLQ+Dzwlu8AfvsVwC/fCDxxV/7nZiM56C8LqUVmEgPZXaEd0exCg6biihSxrifTi6Idrx41i64uUg4t4+zDwH98ObB1ytz/eduVzHVcUJmxJJ47SDmscEsDuCtHFyDlyOPsKnDygknK959HfFXbjlywzCvzHQ9TzjH9Ws+KkHHFV+2cca8PbYM9Y9cw5VAsBmsTf/y9wEfeKP6224DTlMWOOTe10bpun9bfb5sP4qn4fGegt9Ew5SyRlKPITXVJOdyUs66JHxE35fS1oWq6rYsNmfgqFl3EY4Oor1bmGctERrPuJxf8rhXRTFBX7KIdkXIWiq8a6fUGWHwVP5b1Fw9A9+fnT9RcQKr7MEXP4G12zIwvZCCwSDnGbPU8Uo5VOGqzIn3mvcz82LPaEGnntBldReqtWPFVO3qZnDShTH2epC1uyumzwnS7La9fjj6ctplH7dA6dnlEFSPZ0OuAOA+rxFflNSb+u9Im4Bjvo/XoZvu1yabZT3f72kDIz0H6HJ2HefciM2bKUfumjJQTm6ScaOqmyHH116tRs2xNWSykK74KkMZitk4Ai686LNpLpfgqeW5FMj7qWa8Uy/vcO4HNJ4Fbvk4vg5tyOiy+qt2Gm5QzE6/xqLqdM/oay82+k4va5Mg12GCknIrxVUnMznlXrBuZcoqoF4kmlwDlvwunFUg5001xX1ZktivTX/448Bf/wv1amuj1LjXl0HVzmnMvxfpfMhkuHF/F2kyrJfopdQ6OGVWQXTM4dSmRyygicnFNt+W9wrB83T/1h8BvvbDeWNx8Yj76yqCHddxjPHSN7Azq73/ej3WHS4zysiYl2P2Wr6pMlKD2o8zwZMpZzd5fVY2vcpFj8tb3IMVX8fuS6SaLr2Lt14d6GxQUdMkqmHKCghbUSl/f9H7ls69wvmc0DcWnoKCgoIVVlZSDVP/oT7gpZxcMFymblcNn09K67LX2nJSzrPgqeWzr7ONAylmuaCAeCKSc/aIi/DQNWlJ8FQ261SblsOUBi59jd70ZuOePxd/KlLNLpJyi/qWJ+CplgMoZeE5TNpBXcDy2TwKHbyh/HyCWt361+PuZ+4U55TnfJAoFZx/O/9x8RxdOS0k5HvEoLrlMOWqmaMHnyLSxctQiGjhIOVT4OnkvcOZB4Ozny0k5iZwt7kPKUbOP7eKXLJ7Nx3o7166UdKoa7acwvmog1iGJzZm2FPOUdz0umt0NsIiPElNOf03PoFcRMqx4yHX2EeD1z3EbwlR8FdFWHKYcijWjbZpPRDFB4eOnJp1Fmb7ZduXN3qXC7JaMG4nnLL6KEQKiqfg/nR/RxCrYNnz/o0w5nuSmPEUTHSdk00Doe7gpZ7at46h4LBXtFzIu0LHushnk3DzDRXErW6eANzwPuPttxetMhJ7IMit2B4vHVykamNwPNimnCYMVrW/t+CoeZcEK9Wr5E0bKYRFFefFVeSYm+5rW6ghzQC4pR65LN4ecsfMMsHZV9rMb1wKHb9QmBDrnMvFVrA3a+sTvAqc+Zz4329F9bo/FVwH5ptKn7hamjEPXm8/31sS6tNrabALo49A0KccZX+W4RiTMlJMh5Wzp/gHQRfR4ZvWHcvnKlJNHyqFrszxHgBJSDpFqOqYph3+nS76knE+9FXjkfdnneSxknEPKoUgnWk/AIuVY8VVEPjFMOZ1sv0Bkkpu+Wjz//l8VjzczU846N+UM9LHgEWSAScppd/V2xHM3KWe2U0DK2dDnuTO+Ko+U0y4h5azkn+9cZB4poj5xzZgpq8yAPx+L47UQKee0jnyyZRuxiq7lymSfR8pJxX1HEun+bKH4qiTbz3DykUHKkedqp2cS1pJIGsLaAFoepJxtYSjq9MsNUGcfFqa0OiaERUk57a7YJtfvz/lYG/Nqm3Ji83pS11xfJm7QpO8Cqu8Xvp1F50iaatPuqAopp+t37+Uix7ik4qsWvKfbLXET53SrOL4qmHKCgi5LBVNOUNCC2hjqH5B//M+/yvmeB08tMLMjKCgoKEgoM3BTQsrh7+U5z7thiuGDVWrWpBwE2RekHNovu2iOSB1kg6XFV9UodvkW3YPqKU30YORu0KqCyqWK2f1sm+eknCRafOAoE1+1YD9491t1oXbGsOzLkmHaK6GKqL8XNOXk0lliqJmScc5AapqKgvbhG7Pr5VI00XSCz79XPPfif6pfy9NspIkD3ZXigV1jILIGKccwGbiuJ1a/QoPHq8dM2oeLWEMD6WQOme2YA+qufUDH18cEwWfqcvVZ8YxmgpMhwmeAPYmBRz/C1rPIlEMFvJm7oObql6mttR2FJFKhKUeafSJZCLIpFXY8DWn7lPhel0mB2o6asSv7lh4n5ch2QdtJxTUew2cXx4B8Ug43SNFxJiMIzQoGWKF5piODugPogvUSSTmZ+Kqa/WE0BY49R/zdW80WfMhs0W5rggUVr6eMlKPiq4Z6WYDoZ/iygGx7JTPAiQ8Ic+G5LxSv89bT5jpys4/vbO08URGT+hki5ahjisWPJe2HheKrbFKO3OY0tUw5dnwVXUs8TJMZUo4kD7iKnEZ8VU7RcPsZbSTg+u//EviGn9X9A7VlO75K9R+WYSNNgb/+GeDO3zWfG53VbavP4quA/HiiEx8CnvUK+T6LlEOEuGhiEmroddpmV9yiU638+2M7goU/t306Sw4zKCSyfU43tYkU0EX0aGKSclR81RG9DS4pUs52dgKKk5TD4qtoP9E+LzTlrJdHlMVzccw/+OvZ13h0D5mCANE30z7qDHR7pXY+uSDuHToyho+bR//4e4F3/UQ5KYfIJEduBA7fJEzPh64Hjt6il7Vxrf6729dtvMWOIWCaTDjJZHxOnBur8lyi6+t8JEk5OaYckovgV0TKSRPL2MvadXfFzwyTxvI67RtfxfZ92b1RNBHH1scclKf5JJ/0k1aJr6JrksMQ3mqJZdE9DZlyFjEc5JpyyNAyzl4LuMErmcvt6+jX0hjYOeseM4qkqa+/IY35Jb8RZ8wcVFVzti+riBsaifRkK5qIa0BdCkuSQNwrM1JaXVphmWyTpyI0Vlxv399kfH/bsbH9VWnkioH3/ltxLVJ9vCel0HfChoqvOiikHHbNmmyG+KqgoKCMgiknKGhBDbod9DttXHt4iF6njde84NrMe06c8UTqBgUFBQXlqxIpxzJ90ADDMgohLtF38EFRGqDdDxSWvSDlGIPSS4qvUse7TnxVIOUsVWnqj8IO2h1xwkSGlMPjq2JWzK6J46bz36cNRDPgA79WPKCZxLoQrAZ5l0jK8Y2v4sX7uu2cBjHzCgk+MSPTLTH47htfFc/EwO7KFWIWa6cP3PhyuR4Fg4WcPLAnpJyC68mYTDkWKYcXi0k0uK1MOdsepBwyQZApp6DIoGKliuKriJQjC2s+haSH/w5487cCpx+Q30MmB0d8FR+4d0ah5ETQ2OvtTcqRphjaj72hLoSXxVfRdrgiSxQpx46vWtHvUe1CtgmKmuKxb3ZxzC7WGPFVnJSzbS2TE0EYyYBIOa0Wm5lOhbxW8/eitM+oPdYpRAKi3z16q/ibIin48jhVg0w5Kr6Kk3LkeUafr0LKITPA5/9Wvq/kurP5lF53/n5Fylmg4DFnxkNAF9B5UXbRe1lav/M5pJxoCrzxZdowaYsXYjnxgD4LaHOUTfVwxVfl/TbImHI62bhg9V5moLFpS6Sd08C6g5SzdlyStWQEhoqvmpkGwd4qgFaWtEUEk3Nf1M9NLohrIpmq6LP0e7E3zJ4zm08B5x4Bnv0q8X87vgrQppyEFX4B3R/NR9BxI4uQcrgxwor8eet3AX/1k+Jv9RvZMu9EM7GeBilHRrVENjlMtiEyc+SdP/zaTPvGi5TD4quoLyiMr9oQ7bPovuGpu8W+fuqerHmAG7e5savV0kaI7jBrJjl9vzYo9plpazYSZq3zX3SYcrgBUxpSqS08S9Jybv5asy1tXK3/5uagNjuGgD6Ho6mkDcl9RqZEMvT27PgqlynHMmdx8YhBQLczX1KOMjcV9Ns0Ward1v8vEjdllRk6lCF2AVJONC4wvSf6uJRdyzm9LbZi2lTEGZly5HFbZGymzJTjijJsd5lpOzInsrW7ggr4G88DHnhX9vvo3CJSTtlvROqr69yfRGPz0VdJpM/NPBMpmec6HsYil+z4wrLfQ4uIT3IDdN+9ECmnYJtpucMj4l6PaEqAjq86+wjw4d8EHno3M8/0/IifvqScgxxfNT6nrx+u7a3T5oKCgg68giknKKgBfebnvxnv++mvBwDcfu/TmdevOTzMPBcUFBQUVFGE0VU/xMriq6B/JNMAQ6uzO6YcjjFvW6ac/WBI2JP4KjsuA80TU5oy5eyHY3SpKQ3xVftOqpg9yA5SZkg5C8ZXcZMPUNwGnvwk8Pf/Hnj0w8XLowKwiq9aJinH05RjkHJq7is1SJdnyvFAjtPgnyqqeZByukNNEbj2RdpkUTTQe+ExXeTsOoqaxnd4otJtuUw5VUg5K1eI93TkILnLlEPXaWXKGZnb4jKIKVLOYcf65bw3N75qpItOVKT2ac90Dmw+KR4L46sYxQXM1FRErqM2bJByrCJUESkniXVb7K1qc0pZfFVUYMqhbZyPZGwMFQes39stRp3YOWMuN55rowBfX36e82POz2UyAW5TfBUrtHHzAaeT0P1fmxVv61wHP/x/a6OKLSp+NBJfdUwUYPprYp15LAU3WwzWZXwVmXIYKSeOZHyVZcrhpBw6HvaxoH7omfvltpScCxQl5iLlLBp1MBuxIqZct+6wWeoR7YfJBdPYRNp5RkTqPfkp9+d5IZYTDwC2L1b0+gJZmhTfR7mknAjGby8iedQh5cSR6KNd8VVc/VUWXxWZfVu7LdqVTVGh7+GEpU05RndIxo+tHDGPq21EAIAT8h6ETDnGerH2PGekHHsixnycjRvJU4sZCW0546tiUQw9dZ8+B3hUCyeAkXFuwAwadN5Nt8xzkNODiq7rikDD4qtcv83p87MdsX3cBOBLyqH1zNOJD8rv2BJGKi4VX0Wk1iCMmwAAIABJREFUPAdtqds3++80Ffei179EPNdb0+3wqU+J6850yzLlMPKLIuVM9LXpWa8Qjzd/rbl+nJTTYeuRR8qhiUa0HXTs147JbSICn+yby0g5GVOOjDui62eGlMPiLltybEZ9dtXPDKPirK3xmjxx6oQvKacjSV51YoTmk/z1T+z4Kg9TTjxFhvRFn1WGWiLlLFCgr2LKaTFTjupT5ubv9XYXePozYp9efCL7fXRc+uvSiFJybz9f4PdaXVJOwu7R8kyk0URTsWqRcqz+v+z30CLKI+VU3S9VSTnUT40vWPFVkT6us22oiNN215OUUzDRZHIR+O1XASfvO4DxVex3DRnHAXN71W+SYMoJCrocFUw5QUENaNjrYNgTN2C/8X1fkXl9Mg/FvaCgoKBGNDikB/2LBjdttLdBytnl+Cp7gHY/GD72kpSzzPgqdbzrmHLYQHgwjTQvI75qgf2bJAjxVw3JIOVYx0SZaHriNWUUqUvKSfTy+He7FLPCeZ6SCJiSKWeX46uK+nA+sLZwfFXO9nBzS95AHi/Etj0GmYnqQcXwG19uUj+c6xkBZx4Ernqe/K4yUo7nbEhbVEzkJgMfUs7orHhclYWq7lDOVrYKI4C+Ts95fFUJKSe2TDlF7a80vmqkC45V4qton9K2KrJNgSmHSDkG6QMlpBxeSLLMzUT44TPv+fu4QULFVpXEV/mQcgCB0uf9mPhi9kimnGfM5XK6Td52GaQcbtYhU85p0Q55JAUVzCNGygEcpJy6ppzfBD7zJ+7XqpCb8pSmok11V0QB+Wo6t4esuMjMFoMNUZh3kXJovyhjEouvonWj9usqCvPnys6FLVnw4FQCQOz/heOrRnrdAdHnHLqOESwKIod8xftGFy2HzgOKjrBlkHIY8QBg5x8V+sm8Y5Fy+HmVV0iLZ+Z5XkjK4aYcMqiw4zg6CyB1x1dx9dZMUo5NG+uvZeOraJsvPq6vl1QU25CmnJf/j8B/96f6M5zgRDrxQWFiueYF8glGQVHbNhBFf7qHsiOLDVOOBykHcLcnI76K0UUevEP8TW2EX2v4b2E6Nw0zBplyNk1TDv/d2ikotKv4Kk9SDr2/w005cp+76G4kMnHax5nrxIcEUQcQ1ByuvPgqwCR4KZrcVBi6JheA618q37eqjSqPf1yvjxEd18n28VTsB4DnfSfw8v8BuO3bzPVbOaoNNjy+qm1FQ3ETWbut7yfOnxCPVDSntrclYyBXjmT3l9EOHAQ/Tsg06L82Kadltuve0M8koIxFntHisyqkHMsQW4uUU2DKse+ffOKrkkgSjhymHHrPsuKruiv6vsUw5bBrhrpuzKXpiEg5HXGfD7iN51OblLPE+Coeg1dFCYvjKiTlrEh6WI32Yt/jc/Nx47LiEOuacoyJEh6kHJqAMT6XJeXQMZlumzQ0n996xm9aq+2fPwGculf06fFcXjv69Se97KY4AZQb2kJ8VVBQkFQw5QQFNazvevENuP3HX4V/kOQcABjN9kEBNigoKOhS0MoRHVlQiZST6FmDuxlfxQeclClnN+k0OUoXMK/U/k7XAN6ySDk1rruBlLNcpQkj5Sywf9/4YuCuNzWzTpe7OL3G7pc4KceIr6o5cMSXx7/buV5R+XelsZylnLLB5v0WX7WgKSeN3ehvH3OLEd/iOL6Z909E8Y2bctpdAK38wdpzj4jvv/rLxf/Lih+xh5nIJV74U/Ig5YzPiWswmWbINBTPsyYAWgaPr+ID/67B9YwJwoOUkyl+UeF2pLeTTEQ+hQfa30SCKYqv4pESvqYcl5nIO76KSAFyO3ormgxkx1iVxVf90fcA7/lZ/RoVX12mHF4cpiahTDls1nrHorPY5gJj4JyTclj8AqH8+f6mIjbN1qdtp31C31X1/itNRWE9jxZB6+gyifkW+Xi/8QNvB77x5/X/o0nWhNQnUs4F8f/ppjYnUNQQUSJ4TExkzXi346taLR1hZW+LS6WknJL+5uG/Be76ffdr87FpygGA//Y/AK/4n+W6NkHKmeqC+oXHsq9XMeVw4gFgnn+0vgCy8VW8f865XiRzM/6ICKTe8VWsH92R8W+u+Cqu3oou5CYW5QSQbdDqP2ib00TvTzJuUWFx/UqTgMPNYqQTHxLmNB5VA4h+TBVE5XWPR/wAut1XMeUg51rGn7PpIg/+tfibjHE8RovHPtPrdnwVINqXQSBiheUis63qi1K97i6TLH2eCvjtrj6ORDHhRhlbipSTY8qJ58BjHwNe+D3inH/qHvN1ZehIRVvh/TUtu9M3Y2CISkWmnN6qvCebAY+RKWdHHBcjvoqOsTTrUSwOIO6vXv168xgAwmCzLiOsjPiqrrlfbOMU3U88/Rnxf4raandEu6Q27yLlDEviqwB9T83bb5smN3HanhVf5TrfbVEElC8pZ1qFlDPV+7xujFAlU05RHC9bxnTLbHt0H0D32I3EV6XZfsag003Y9YyTchhhLY218a/d0dcGlynOIOV40GHU/VMdUg7FV1Ul5XDyTyc7CYWW3VuArOeKryqKYF5EGVJOzZi2uqSc0TmLlDNn0X5bLGbKM76q6DcttZfpljb6HsT4KqKZAnp/kgkeqL49Jz4k6EFBQUEHWsGUExS0BH35dYfx7ONruOf//CYAwL9712f3eI2CgoKCLhGtXKEHpauSctoySmo3KBsuAwoNguwGqadMtF/2JL6KkQ2aNgUtYjbinwmknOaVxHrQb5H9e/5R4NwXm1mny11JJGd897L9Eg1adqWhgwp8TcVXFfU99N6iQaokEu1ofJ7F2ewSKcc7vqpmX8+X4Rq4NmJGykg5Q1G4KY2vmsr4qqPi/ze8XPTVRQWNU/L3jS8pp8n4Ki9SzjkdXQXogoGLlGPHV81HYh2pvbraFh+UbnWK2x+91ybl0H3BbKcmKUcu1ybBuEg53b7+zEKknLbZtmm9yXTB32ebcpQZh0W/oJUtqitTjiwkn/oscObz4u9oCmzIAub4nB70t00HTlIO68cqkXLY+cPNf1unNFKf1O2LgfZ4xqKbXKSciv3DfKQjU1yibSNzFO33L34A+OUbtXGrSNTf2CaZ7lAUmOz2MNgQ66Niq1JNTooj8T6iRPQdpBxlynG0VzIIttrl5wJRUFxmH58i26feArz/V92v2aQcAHjutwA3fZVev0XvGeMZcPxLxN8XXKQcuX/VpARLTlIOxVdZxifbQOAy5eSScuYmKYeoLd7xVazfp3PSJ75KkZWibN/mJOWw76EoI4qv4lFBXL0V83q7/Yz4LEUO2e9Vf0vCTmIXZeV7okkFUk7BbyNV9GWElO3TwGMfFctVphwyUrJJIWRiBszjR+aJiUXKycRX5dxn8P1eSMqh85IMON3stbUovopMmHl931N3i/P0lq8HrnmhScpJErGetN3TLSu+isyCA2YmmYnoqt4qcOVt4jl+rX7iE3JZ29lrKf87notzqcvaS542rpHr0df7JhNfxUk5zEix+YQw5PBj2F/Vbb4svioTq8mi1wDLEGaTcmyaz2o5uSPhJp8KpJyutV554qScujFC83G+USGNq5NyANF2XAZn+p5+U/FV1vgYj+abj7IGZh4nF0fyt2FHv8bX3xa//+sOiokrAIuvqmmUAqobejgdK+/eKxrL+CqPCC7nd9jxVSvV19NXufFVFfepTxQyXy71UeNz+h6htyL+JuPsdFubZ33jq6KZ3m8ZUw6Zfbb1vfaBi69qmaQcOt/5OEjVNnf7TwHv/5WFVi8oKGjvFUw5QUFL1PpA3MRO5qG4FxQUFNSIVq4QWcYA/Eg5sv+lAQa7mLQspY4Bp/1EytmT+CrHAF7TBilFylnUlLMPjFOXmtJEz+qsW8BKEgBp/QilIFOEmG51HKQcwtX3RJ+pitkNxVcVnWM0iFdo3JGvbT2tn9s1Uk5RfBU35Xj2r7OR2RfyQUnXwDVv/3kD4LwQWyW+6rZXA1/5I5om0B3kf/b050TbOf5c/V1FA+11STk06G8cXw9Szuisps4AjJSTE10EMFLOjngvGQNcRSBFv+nJwnBB+1OFUpvywCJOpltiPYq+05aKryJSDpkmBtn3UqEvmsp9xaJYgBxSDpmJHJELpDJSDo9Ss2OrWi297z7zX4B3/Su5HRYpZ3xBH/9oCqxTcYCTcmibOSmHTDln9GfpsWvto7Z1f2jE+bBzeTbSpqLtU9lIHYp6Mkg5DcRXUfwMFd9t0bEi4gMVhM4+LP6++Hj5d3BSDpcytFmmr/66OEZ8ncigQ+eZvQ8MUg4zD9paOSKuT1c9r7zYRNeBzHL78hpWUsCZj4XBwTmDfmSaMGy1mPmrrqKpIGX0N9zxVbRPvUg5jHgAZI1WdtSOK74qr+AV26Qcun/II+WwiCfAPI7b0pRTSsopia8abGSLxbwYeu4L4nHrKWD1eLZtq++xSDmnPyceVXQVoPqWPjNpdSWFIrWKsh1pmpiPqptyXO3JiK+S3/HgHWLZt3y9Jgcq806XkXIS3YYMQgon5bhMOb6kHCD7m9wRX6WWz0wAdOzsayMX0WxmOaacEx8Uj896JXDdi4GnP633F7UduheYbplGAxVfNTDb6ZOfBK59kW5vdMyfvkech4dvFMtOImZI4qaclr5mFfUfJCp4d5gphxurAE1/Ua+x7bjqNnN5vTVtViwz5dj3QzYpR7W9Vo4ph7Xr7lC3pSJTC2Cajsquh7MdZlgu+R0yZ9febg2TRSxpMepeia976jB5FNEz2b3ubNs0FbbaYnm0foMlxVf1huJYxnOx7rbJ1IivmslJNJ6mnJkdX1Wyr+k+uY5hxTbd+irl25NzvZpPJCmnJoUlY8qsae7xEY+DB8rPtzz5TPAAsqSc8Xk2KWFVGLno2jnb1td+X/NMPNWGNPt3M/XfikpZIRZrrzXbEcdmeMgi5TjiwKue89Mt9/kYFBR0oBRMOUFBS1S3E06xoKCgoEa1cgU0caXgfYTdVUQY+YO81dodCooLH06DIK5B/93WnphyeBF1SdfHZAFTDh+kCfFVzYvHV9U9B2ggt64xJMgUmXIIoc4Hn9MYQEsMqiURyz2vOVicia8qos14xFfR5w1TTtns2Snw/tfXGyitRcrx6F9H54A33CYMCSSO+3YVw7ziq+TnaJC5qCidxHI29xC49RuA1/y6fq1TRMr5HHDsVv+YAJ/1dqkuKWd8Hlg9qv/fk2YJTnAg0TXJNuVQhE6RKafdkwVdH1NOSXzVYF0X8HxmvdLg7s5Z8egVX1WFlMOMR6TK8VVUmByy+CpWFOqtim2//y+Bz73T3I6pHOCf72jDVzzVBczROWbKIVIOj1EhU85pc7nOCLOOabjjRSN+Ts53gKO3iL+3T4ll8YJypy9NOVNthMhEB7Wrm5KpqF5Gyun0TIMBxX6QqadIqt+wishddu4Aet8NNmR8FTflyO+h2dK0LCq6UJRGmhaTnY7eCtz01eK+v2xWvYqvmpqPihRW0hfPx+LYj87q5x56D/COHxK0iUJTTkOknM4AOHy9WbwhlcZXAbpAx4gHgN53PZuUUxBflUvKmVmEDWkayCXlyHVx9Wl0Tq4dd38Xqb9qxle5SDnTLWFA+Ov/TWy3QcqRppzNp7XZ1KWubcq5XzwSCY6LF7XpumcXZel9PL6q8Icre91JyuEmCPkdn/2vgpDy7K8Rr892zGuNQcqR56iLlBONTVoV/91aVFyeepJy7PO33dHX5u1Ten3z1C+JrzrxIXGc1o4LU858R5PV6DNk6EjmprGLTDkdZsqZ7Qhjzw0v1e+ja9fDfyceb/0Gufwts1/nZg26n6hkymHrQecoiZNyWh3zumy30/6qvj/mUYAkagftnoOsYp2vPJK7zJTjQ8pRpv92drwmT9Mt3VeUGTr4tbcOKUf1A2n2Nw/dP/FretG1PJqY7dd1L0V9b54xoYqcppwVcQ4qaqF1LXDGV7G4J5Irvur0AwBawJGb/Eg5swVIOXPLdOsr+q0LZO/zSJGMqezmGD4++OvAfX+e/x12fBXdgy2Dzm2bPDs9AK0a5jPP32T0vkPXiUfjvnso2syckXLIPONjiAZEmxnkUKKc8VVyXGI/jKcWabYj+uHBYW2QBHT79Y0Py1v2MunAQUFBu6LgGAgKCgoKCgo6OKIZ5AD8SDksvopmZO2GKcdFyqFBqn1FytlF84kxiLCs+Cq2vKo/1gMpZ7lKk8Xjq6i9lg36BfkpkQOvyixlGdParM9UpJya+54GJr3iq3xMOfI9mxVMOY9/AnjfLwKPfaz4fS6lnqa9qqScB24XRWxeiDWQ4i5TDp/dmDOQR4PXPkXpPDoGfT7vfDv9ObMQ1F0pHhSO52xGp2c7iiM92GvMCvQg5YwvmMWorpwtnMbZwq4dX0WmnP6aNFkUxFd1urL4WmDK4e/l6vREvzjbEYPZg0O6mORFypH7UZFyCkwOypQz9TflKFKOHbnA9vV0SxQsefGGv0+ZAlZZhNGafl9/Tey7i0+wGaSMlEMmDypwxDOG0fcl5VjxXvHU3QZ8STlHny3+PvkZsX7rV+vXu0OxfIOU4zLl1CXl5JhyONWIt0cqpCnKZIFUv2GTcogyZcdXrYt2y0k59LeKr7IivFSU0aS47/n23wT+yZ/IPqjgXJhsim3srWqzz3ykaRw+s6rpXCODAADc/07gvj8Dtk9mo9kMtcoLb194v4g6y1M0FcXA4WE3CYmO+cgjvkpdz4mUw0hVgGme4OeyUSDKuV4kkRVfJaOUbFMvLa+QlHNaXA/48lzibdk2wAGStLUD3P8u4OP/ryBDzR2knM2ngI3rCr7HMuU8c7/4zclJPrTvuCmHCt42KYcv05uUQ32xoz3xoq+K/ImAr/0ZTUKZbpnncIsZHiYFphzApFVRG+oN8822cWSdly3r0RFfxZc/PCLaBxUr7esHlyLlOEwB8Rx47OOCkgMA171IPD79afMzZMoBrPgqIuUwwsuTd4k+/LqXsPfJY373W4Up89qvEP+f71hGN0bNoWPmIoHZuvlrhbmqwyhCfGIPYJFy2uZ1+aovM5fXW9XfX0TKcRl4lVmY4qssAw435dgTbXpDd1wdlyLvMINZ2e/u2bY25ZQZOvi1tzuobgAxCBbWNijyGDflFMVXTfU5N9u27qVaJk2Q2mLjpBzZh9qm29z4qkQflzJSzhN3ioi34WE/Uo4iHtaJr6IIrpLPzifCoEmRQdyU084ZB5yPZR/Qd7fbu94s7gfyZBvvu0M4TV1NiMfJAdBRw1VNORXjq1aPi+0bnxPb1WpLmioz5cy2tCHbxxANmKScIlMOj68C/Aw/e6nZjtiuwYb5G0L9DqlJjwVEe11WPFpQUNCuKZhygoJ2Sac3a9x4BgUFBQWZ4qYce2YXl51RniQM1b7L8VUKZd6Xg9cLfP/WSbMIXVecILRrYmSDpcVX8YiZBUw5gZTTvNKEzdoNpJx9IUKwq0KQFdFEMQk8vqoujpvOKZ/4KhrEKxrMo88rUk6r2BQB6HZTZzDYiK8q6LeqmnI+9xfZ5fOBRte6+iCvI1aYKytKRzmFeEAUil3rMNsBzp8Arv5y9t4SUk401cU1X2Mdj6vgx9eHlBONzRnq3aGeNZ+Jr3KRcuRsa5ugQOKGFSIi5CmPlAOIYt98LIrx/YqkHGrTtumk0JQzE/vPi5RjmTDo/TYpx6bkALLgFJvxOVTs61umnJk05dD28PgqMpPMR/r5lSvE9ozP63PHLjSJjRIPdnyVk5Zkk3KoULZunpPzkSjud/rAp94ivuN536lf7/ZF+45musibia+qQW0sM+UYpBwWJVeHlGMXkfNIOf0Nccx2zmgDHH0PkVKobVBhmI7TfGwSbWx1B6Jt8G1xia4BVzxbf+98rI1fPrO1abu5KWdyURTfX/1rwFf/WP5ny0gJSQL80euAO383/z1Eyhkcch9fem5ywW04TxPd7lVx1TblEH2DzDt2fFUdUg6LSLLbsxFfxYxYpJ1nhOGl6LcckDXl2H13f11ej2TsFy8+H7nJjK8qIuUQwYl0+n5hOjXWr6XfSyLTGB0XO25oPmYeUs/4KqdB0hFfdew5wPO/Wxf9p1uiYNpqi9n5LlKOEV/Vd/+tTDmr0mTouF7bBpkiUk40zS6/1QLWrtKmHJc5hNSXbc5FynnqHmGMefarxP+P3ir20ZkH5WfkubPOTDn8u6gY3O3rdvrAHQBawiRDoj58chF41U9mzWmANrrx5wA/Us7zvhP4wb+S65cTX2WTcvj9xJWWKYdfYyubcqiPlued05TDjQHsHPEi5Tjasv27+z0/B9zzdv3/6TaLryq410pTcU2ifdVdqX7Pz5dvb4OizbD7jTJTjjrnUve9FPW9ecaEKnKZcrqyH6LjqdoRM+VwM2caa4IRPb9+ddaUkyTClHPjV+rvKdrXacpIOTUMBb6knIf+Rhg0H7hDryddp/LiFucTcUw7fffvqtl2/r0XYPbPALvmLcM4wX//SNWNaVN/e8RX9Ybi3nt8Xhtv2j2xPtQ3T7e1IdsnOpmWn2dIUwSeLTO+qmyd94Nm22K7+DW3v5ElOrY61v1XIohsefeVSSz6uEDKCQo68AqmnKCgXdJkvs/xekFBQUEHQSscwVxEyrGKTJQn3QRm3keuAadOP38wwFfv+gngnf/T4uu35/FVu0DKWcSUsxtt5HKTMuW06hvTqL0uK6f9clM8LyfltDsNxVfJc0qRcgraQCVSjizorFxRPkBFJp86phxfkhaP1yibITg+D3zhH/TnSHwfu4rR3JSWdy7wYndZUZr2cx4px3UcTj8AIDVnZxPGPO/YxrPqRQc+CM6LgPwam2fyjGYWCWCgl2fTFmggnb6DCrvdQZagQLLjq1yziNV7GcXEVm9NFBUpvqoKKadKfBXti3gOIGWzpImm4GivrvVue5pyaEYyL2LR8bfjq8bnRJGekPR0DtikHE5WWTkqPldGyklTBylnbsa10PpywwN9V39NfwcVlfprokg1Pg9c8wLgyufqz1Hkm5OUwykKNU058dRtajNMOcwkNqtiyskxyXQHoriUia+Sx3PzKeDwjfp70lT3hde/DHjd7+sCNzdoUF/mMpGp714pLmwpU87N4nE+FucT7XOv+CpZ+LFNOWtXAS//5+IY56msKDvdFPutqKBHpJzBhhkFppYhP5smbpKOQcqhWeQUX2UXkZlpgK+7MWvbcb1I5ax/XmBqd/LjZ3h8Vbsrvo9fr555ADh6c/Z7bGXiq1ymnG1hEgVkPyGvm1c9D7jwmDhnR2eBQ9fnfw+PIExTcY2z6SPqvTy+amiRcqwon6gGKcc2mNI60XvWrhT79B//H6J90zGZboo+aXhEHJcWMzxMN8W5xPcfP8/5tZJHsOSZbTPUmhJSDqfWqUL/lYyUUxBfpUg57Bz61FuAJ+4CTnxQ/J9IOd2+MNOdechcT4OUkxdfJfuhndPAjf/INPLQMT90PfDC79frBFjnlMOU40PK4SIzp7q/ITOYRTOiY9np60hFe7vQctOo6JrtvCdhxknAYcpJrecs4oq61yiJr+L7y76v/syfiH+k2Y64z2+1i02aNn2tjlmBL7/UlFNwLY8jsV18/7cdphzaTyq+agmknHiq+1H7WtBh9JF4Lq4dnJTT6gBXPz97j3v2YWEUveHl4v9kSM5TPNPHeRFSTtlnKQb14mPikSagANn7V75sIuW42u20xJSTia+i+/glTMxW5w835dSIaatKyukOtQmWjDdE/6T7ktm2vvaXRSfz9VDxVdb7nfFVffd795vmI9EP8752eIhNDpDrP1g3j8UX3w+87bWa9mZrEWNbUFDQvlIw5QQF7ZJG830QVxIUFBR00OVLyrFxyAmjPuyKKYcNnqpBD0nKWcQIMzorilCLak9MOY74KtfA8yIyaBZVTTlsXQIpp3nRQOEi56CKrwqmnEZESG8ehUBK5cxCokfQwFrt+Coi5TQVX0WknJPice3KclIObUOdQVJfClcc6QHvsv71gTvcfTEfaCyKTeqt5u8jg5TTLR68zKNjAPko92fuF488vqrnoCEY680oC760K2Wi6eqCAqDpKcPDyL2e2BFF3aFeXia6yCblbIvt6K1o2kFme+Qx63TFoGqhKYcG7B2Fx96K2Lbpdg1SDhFlLopiSCEpRxZdIoqvkvuNCkGu9efbSPIm5bQdphwi5bCCZn9NF1ABcdyofUy3gMl5ud7cFDJgM3bJlGOTcloAUkkXsfoUm9wAFJNyYvb5NBbbQZFVz3+duRwyshFpCXCQcuqYclj8lCvGxYivYgYDZcrxiK/iVCOunoyms8lJdBx3TgNHyJSzaRrW2m1B8+CFfqCclEMqix8heiQZPCI5g1iZcnziq1yknE03YcJWmSlnwkhPLhHZoTMQBRsnKYcZcVy/AXghls5Vm25H+9iI2mHtkPfJToNeDCCVx5zMZZ3s7y0Sp1G1WiZxLI4EieaaF2a/x1ZvrTi+auAw5cyZKSeJgMc/Lv6/UUDK6a5IA/JcGEWmF0UsC5fqM7mpUBZDE6soC2hChauI6lTBhAVV9G0Dh64D/vfHgOe/VjxH/e90U0Sc0W9lut6kMr5qaJkzuBHHINmwc7WTY8qxqTWq27VMstS+VxymnLWrdPRikSmnOxDHnb4zSYDbfxr4z/8EePAOQYnhBprjzwWeIVOOvK6VmXK6A22GAYDbXmOuA0UnvfInhPmgX8GU06toylk5IraXvpOWxUk57Y4+F45/aTYak645w0P6/oaLjCKuewUVX0UmNR431RZt3TXOQetH+zHvHoafK3mkrekWcO4R+f5EGC376+XkG/saVseswO+77XvVKqYc+l5+HXHdS3EDMOAX+ZMnpylHritdO1zxVcrMOdeTMgDxeMWzgNWj2XvEJz4hHm+UppzOoPjenn++7PeaSz6knPkYeOjd4u8Lj4vHlG2Pa3IcGZGI8mTFyfIuAAAgAElEQVT/XopmYr/4kHJUfFWF+/iqcpk8O4Pqv88N6qoHKac7EOfgdFuScli7USRHek0avbziq2aahpYXXzXZlMeRG8j2OylnR7QpRYs8JNsXmXLovN8wt4XO09FZ93KpDwqmnKCgA69gygkKWrJ+9OtuBQBsT4IpJygoKGhhcVNOESlHFZnlD1ce0+JCvzct/oOZk3IWNQXF02YMCWrW917FV9HMuKbjq5oi5QRTTuNKYhN9XncZwP4fiDkoSmKTlMPbfSLx5e2uGV9Vd99Xia+i4nKhcYdMOXKW9dqVHqQcK86jivh1o5DyM9eDsXnrv3UK+Kv/BXjvzwGHb5KDqTkYcVd/rwbw1/OPBzfa5OHY7eU5STk5Bbnzj4pz+chN7L1kyimg9+QhyvNERbi1q8xB/JEctFw9pp+zLyfRJEsCUKacnPgqZWTYEQWA7kDTDmxxE0R/PTtg/8FfB/7qJ8XfysxQEl9VlZTDCyCjM+64KRIVyuKZWbhRppwSowfJjgGdbpnUAON9zJTD46s4aaK/Zg4+85ik6SaLr2IEjG5fmnKY4SZDypEz+im6au0qPYubR+uQ2m3z3HaRcqhA0F9nppzvNpdDkW9OUg4zROT1I0niLmRw0o2LlmKQcpgpp1J8VREpZ+Ig5TAz1uEb9PcUtXdOylHfV0DKsWOFbNnxVdFEmnJkG/OJr1KknNP6uclFT1NOG4UGc9V+88yKct2IlFMUXwUII5otJymHrndWkdonvsrVP3MKFy3LMPU6Cp0GlYURK849Io7T1c/Pfo+t/qo474jUkyHlrIltOPt58X8eX0X0lE/I6LCy+CpAtJ3TDtOp8V6LlBNxUk7HfJ9hyvEk5Xzg14A3v9p930HL5/FEypSzJdrH6lHxf/5beLqZJabYJDm1HmTKWc0njVCbVPsih5RDnzVIOXL56zlGGZcGG/oatX1SXPu2T4n4nGe/0nzvlc8VbSyes3uI4/p1Z3zVQD4v19825Ry7FfjRDwtyFuAm5bz4nwIv+yHzOcBsLz5aOw78+N3Ac79V/N+mbwByopGc5HLVbZlFKONYXh+mTDk+pJxUr4eKr2JGM9pWZYSU+zE3vorGAzrZ8RpAXP/mI2GqiKb6uA/W803SpGWTcqg/5UbbXFKO7DOHZaQcRqtsdxeMr0qz/Qwdl5FlyuHxVcrMKek+dD4eul5Qo/prWVPO458Q7evYl+j1LyLlGKacir/D0lTv+6LPPvy3wsA1OARclKYcmoACyHE461qljFZD0XbtY07tzyu+igxqJca0RcTJ06Sy+GCXjAkgnqQcMsHStbhjm3I2RZvu9OT4gcdvvciDlEP3PU3HV023gZP3Lb4cl2ZyogX1tYND0uRqxfTapJyZZai3NQ+knKCgS0XBlBMUtGR985eLAbsffPOde7wmQUFBQZeAvEk5Fg45jbOzQpcpNWOG4cO7fWkKWsCkGc2a+YGv6Ay7aD4xZtXtRnxVxW1LPYvuQfWkyCvt+vtXYa8DKacR0UBly1FUoz6zLYvHKvalLimnSnyVh/lKxVfJguza8fIBqkXiz2h/ACWknHk5KefePwXuehNw01cD3/NmMXhpRIexzzkJLTnIay5emCgrSheRcro5sy8vPg5sXJctuvLluda7OxRtzvcY0CD4xtXmACXNJFy5Iv9eIJqZxf7uUMdfuCgpACPljDTppJtTBFI0kK4YbLUHUB/9CIsnY+QQW701YPMJEQVw1fNM00KZ+AD2zhlxrHihi4vPLuVFfBoMt8kHfPkdRyGJNN1yx2OoWeCsiHXsOeKREyvsgmU8Y4WdVMebRBNdjOkOpZlppM+djPFFknIouurQdawf8yDlzMfi2PZWGGWL4h9WBaXiq/+lJsSQaNA95qQcR3RQXj/y3p8D/uA12ee5EcdVHOLHasCIK9Quxz6kHLl/bbJDd2iRcqz4KgBYPS4MidOLen85KQxU8J3kk3ky311wLmyfEjON6fdBNJURY7Jd+czWpuXb8VW+ppw0EVEDT34q+3oZKUdFeA2AwWFJhLL66+mmbjtlphweQwLoc0YdU2ZaM0g5M2YodVwv+DGlY2iQLgpIOYB5HE/eKx6v8TDl9FYApNoU5oqvou8DTPPedS8WsWkP3iH+v3FdwfewfleR4Kz4KrrWZOKrxm5SDkUfepty5PIf/wTw6IdFgZlUtAzqf8mUQ+cC/y082cwSzTgZhp+DivYw1Oe+LbqWEoFGRfNZpBw6FnmkHPu5PA3W9TXq/KPikUxXFI1HOv6l4pp7/kR2Pe3v4qScVks8XnmbMOHYuub5jJbE9iXt5xe8ThhzAGSiZarqyI2acKPGETgpR772/NcCX/7a7Od7crtyTTnyvHGacuRnFSnHjq9KrOfovGAElrx2AzADWwuZ8RqAXetS4NwXmRl2zYOUY93PdoduU3WRogJTzty6ZhVNtqLl8Psj3ifapByf+/UycQoiySbl2FTBTs8i5SS6zX3f24DveKOOLeJ64k7ghq/UbTEv+onEr4GVjwmP7S04/p97p4g2ve01mpSTMJORawzCmMTg+M1D91KV4qssY1uTcl0Lis63PBmRlUWmHE7KkeYsouHQfuXxVYCOsvOZgBHzCRvWvqc2Q2233Ws2vuquNwG/998Um8nqiqJuDVIOo8/Sd9IEG7pmqt+hOfRVMu1UPYeCgoL2nYIpJyhoydoYiBuV7Wkg5QQFBQUtrMqknFjPvrJnhS5TuaSc7mKGj2jSzA9HWoddja/iM3uWFF8VSDn7V1Q0ajURX7WEmWeXo1R8lfxJaBhDYtZnNkDKycRXFZxjPt9FfRcV3NeOi8G7IvrWIqQcmvlHfxd9hzLl5Gzj+Jw4D77vbcANL5PGJx5fxUk5jnVVyOv1/OuBGmReKR8YjVhR2FZedMWFx7NmBJvwMjpn0Remcja6R5wMiQZ6N64146tG50QBu9OF83rCI2Hs9QOyhShVSKVB0W1NOumtumfmqminniRcWPQSiqQCGHEmJ77q5L2iXT3nm8T5mLffbfFB+NGZbCGci0xTavDXKi46STkOM1Hl+KqJ+K7uALj5a0TsyhojHHHiA20Tbx8Xn2DfJWfjkjFgPmakHCKBMGJDmmr6yeEbRJtIU/d+IgMiX4/uUBbJ5PO8OPiC1wHf8kvZ7eZEJjKFueKrkLr7q1OfFcUuu8BikHIcxSFONRoe0e+fVSDlzHMMet2B2NeclgKYhenhIVEA5qQclwlNmc7G4vzo9N2ULhKRcvL6diKD8OXy+Kqy2dpJos+1LWnKSVNpynGYzTKS7eyOnwHe/W/c6wfkn8/UR3YHJvGEa7qlDSUjX1IOmVDZtQAwaU12fBUZmZykHCsejZaVS8qxTTlshvjJe8Vyjn9p9ntskUFgNhL3EfZ527coXZyU0x0AX/Vj+rVDRaYcFtlz+n5BwiLiTOa9zBzRG0KZhgCLlGPHV3mScnZkn/XRN+rX7KIvF7Wbyaa4v1DxVezYTF3xVWxfOuOrikg5sl8hYlglUo68Dq5XMOX0GSnngjTlvOYNwA/8Z+DLvt187/HnisdnHmSknBxTzqHrxDqvSpLOdS8BXvqDxesCuEk5XAYpZyX7ehXR8eDHi9rZ634fuO3V2c8oUs6R7GuAjusqNE4SKcei4qQJnPRbvp28+GzLiK9qZY0SvP8794g+7v8/e+8dLslxlou/k+fkzVHaVQ5WsJCDLOMoS2CwwchgDBhsuDJgwL7YmEu4GJMuvnABEy4YsOExYMz9OeecLWEsW3KQVtJKWkmr3ZU2nhwmz/z++Oqr+qq6qrtnzpyzK6nf5znPzJnp6a6u3PW99b7lCTMWhTAMpZxYUg6THFMo5Wj7KknKibGvKlTiSaT1BWPLFgKr0kpwWl2lHD1mFG0yZ68jiDZKDaU8RgodvKa2dJL6ybOvMdcpVmjcCT37yDlmv0o5TEDI5ePL86GvAhfeAGw8l/rRVt3OE5d8DdiWZIVS2EKptRyjcChI+oDYpLAGm4i8pJyY9haC9awZZ18lyfBjSimnrVRrWCnHef7RRK9e/PM+z8ddci2D857TUCgP175q6QSdO45wlRb7Pgg8+m3zf3OF8qvqKOW49lWuShAr4fg2SgCGqMR2mxkyZHjMIiPlZMiwxhit2A+YH7j9CB46FWC9ZsiQIUOGeKRWyhE7N6WkuGu7sFawdpAxKacUDb72i86wlXLWk5TjkboeNkHKUvpYBSlnPSzOnmjQpJx8PHEiDlxfM/uq4UCTcormf4ZWyinaCz+DkgK5zFPZV3E5B/qnXk/8Xp13bKstA+8972rsqzom2JlkvcXB4VD/WpulXeM8hrk7Y5NIObzwWpkI725sKyIEL6ivyr7Kk6dzh4Aph5TDC//zR4DFY8DfXAXc/OfiOipI2xcpRy2Ujm+nPOK01GaAUVYDcHbmA5T3va5jXxVDynGDGM1lRcoYUYFVj8qFJNqw7YxMQ2vFBCFc+zYJJqWMbAR2X03vS9V0QYtO09zX8rTflonBn7cbAaWcOPUVTyCJ0Uwi5axQGuWubIl+SDms9lKsULC+uWzamWvJEFHK2a3OX1d1w6nvbrCGSVn5gskHDhDE2ZEUK4YAE1HKUfPBOAvPlVOUjukD9udJpBxJEmNyDCDsq/pQynHzpjhil4tPKacyScGH+kK8jZpUymksRkkVLpKUo1gZRCudNKjOcRlxfxOad8jzslJOc5nKoB+lnLlDfjUiab/mg1bKKZvgjUugaiwCG/eq8/lIOcKyRNuQOOMdl6lUknLtq7gt+sYuWabSviqkIBexrxKKFcf3AVsvjrctYzC5gOuvS95w+49WTQTOR4CLXghsOo/qQ1x5alJpHZh+wBA7XOy51vTTgCE7cd9gKeUo60P5/BML9f3ySZoXPPRV4NHv0GeufZWEpZQzR0oR8litlOOSchx7RwbncWkkbIvCYxsTawZSyhFEGV9fIVERFpGzB+l14znAxT8QJSptUXY6p+416ZRWl/Ja5z4X+NXvmvb13z4FPOOX4tMC2P2Wr1xXq5RjnUsQ7rhMk0hMSUo5AI3bIaIwYIjQ0ppHKuVYJFM4pJxqeH7qkgrcsVeOb9MPmP8r44qkmcJqh9OSZH/og+yr3XvQannCDrCniAfuGKPn6nH2VT1zjaIiHIRIpF/7v8A/3xCfdlkuDK5/NYeUI+2rJJmz2472M2VHPemuDwHo2YS4JCJKcxVKOdynV6fCv106SWP4ziebTQPzR6L2Ve4aj6wzPnVQSSYKWQq5/TOPC2uiZiIIcYz1UMopVBQ5UirlOPZVjEI5OheJSwM/Q7hzD1ctpiCVcoZhX8UqSClI60n41G8At/6j+V8r5QhSjnym5vS7hCRtXxUgCsnn0czCKkOGxzQyUk6GDGuMHZPmIeyRuRre+L7v4vl//uXTl6AMGTJkeCzD2vGVVilHWkmtl1KOeDjXu42UV/hqSEHtRvih+9O/DRy8Jd15tFLOeirCyF11niDqUC4h1W76PPewlHI+8UbgzvcP/vvHK3pd0QYz+6ozAizp7bOf6HapH2X1iO4qlXI0KYGVcmIIgUnkK7cPz5dM4CFkEQKsUimnYxYYk9R4kuyrarPRXeOWUo60r/IptKj7KMfZV9UNESLRvooDAgH7Kje/Om1g4ZGoUs55zyNFm8/8Do1H9XmyHtC/WwUpZ2IHvfIOwpUZE3j0KeW0RZBD3gsjZF/F6LZoQbRYCQd1LLugcUNAYbRWjL0S3683AKaCzudfJ3bEj6RbzO80jfrD8klF0kkg5XRaduCGF4S9SjkexZNcwbSBXi9eKYdVSOKUAiL2VS4p55B5z4H5QsWjlMOkA50Aus/lU/R+UllmMUnFp5bUc+pQsWoHybh8ywmkHFZN4jRxgNQNYvrmo8un6PXEPfbn9XlShwICpByhYlOdMruZ45RyOm27jwn1BdwPREg5IaUcR1HHOpdQtGku+euORJINBJNyJHmnVROkHA42BuYdfN580agqcV6lIuXkaHxYPOpvQ/UEUo4kRcYp5TAJ0kfKQc8meQKiztZsUpwO8Bdscpi2r8oFlHJE2Uv7Kp/SHh8v+9mSCBoe2wfsuMJzHx5wOXKZuHWq4lPKqamAoUrfi94KPP934kkxUimnNmOTOCT+26eBy2607wswZe8qpAyilFOfB654GZXbHe+lz+LOUShS+msz1Pd4lXIWo0o5hYBSjiZZeJRyPv8HwGd/t3+lHLmpxquU4yEbSZTHTR7PPkxzDddmj1GdpO9P3U/pLI3Z44wch3M5Q8jpB+UxAE6bkhimUo60ppPkgtj0JSjlANTf+Ei8+QLlN7c5XfeEupZF/vDYuhViFGp6DoHBVamTqoOWUs5Ysh3V0JVyXDsdYccJmPx4x/OBr/ypPy1yHLGsQNUcRaulKWW+0Px46TiNJ3H34yPlcP1jlTVpM8Zp0gSKplFKlWBSDpMk7nwvsP0K2+KPlSlDBA9rfjygUk51g9oo4nm+Oq5sEbdfDmzYQ+/nD9mkHN8aBKerOEL30G3bxB05rocUVfRaIxMaBUl42PCNBUnWYT7I55IkpZxCmcZSrZTTMps+gKhSqCR6xT3vcf6URuh+QvZV8rzDtK/i8nSVfvpFr0fPpXy+Xo/ySZJyqoqUw+XUdghJmpSToJTTzEg5GTI8XpCRcjJkWGMU8ubh/65HhsDAzZAhQ4YnMkrVqBe2Dz6lnHwx3vt7mPAq5ZTp/WqIMJ1m+KH7638P3PeZdOc5rUo5ebGQMGxSjmO/s16/lbjrQ8ADXxz8949X8EJhfhXEOF4ky5RyhoNuWwWtWClHKk0JpZyeIBV0W4OR6bjMkwKknC4gXM5uv1UeSw7cyt8Nskja64pgZ0LaE0k5c3aAypWrl8FMX/BBSl4H7asaJiiRqJQTUMfgz9xyWDxKdcJVyqlOAj/4Z7QwftcH6bOVU3a62aomreKSVspRwTtejKzNGGsRH8lTB5BD9lWudZFYFuH2wEo7xZGAUo6Qq9fBdCnPr8quuSR2ensICBw8u0DsgE6rlNNuAGPbqK0m2VfpheyQUo5nAdineMKBJIDqTredbF8VF5TkYE9RBDFknZs7bN5rpZyyshWrmfZYdHZ/55St0PJJpaSi8pl3n7p2bbmcY1+liG2SNMf1r+Soc0gUKiZ/Iko5QqUEiI6FvZ4g5dxtf1efJwsuIBoAARz7qilzXCOGlPPV/wP80/Xmf2lTIFGs0vm5Tmv7KkcppzJJ1+T8irVGqVPakkg5SbvvI6QcRylHj2+BPpD72KmzaLd0q9YnKUfMZ33lkqiUI8guWvHEOU9jke6xMmXUDiRke9bWDmK8k+WpyTmSlNM1hL6QUoMuU6GUkyuESb3dtmNfVaX6tXQSWDpGgdM0KDlKOSH7qtEtdD+tWvSez38+8MzXJlxHkLq4TqUBX4f7KWl5VVSkHL0pIYmUI55rN51HgeUFpRTGfUWIjFGZILUmmYacJOUsGFKfTl/F/95Syqka2z+AnnHu+aipo0zKSVLK8dlXjfVhX1UZN33Z3MPAhgQizZaLyL6quWgUVvS1ElR50iCXM3UviZSzWqUcLvO8IOX4FJMkuN0kKeWE5gsjm0xfE3l+Z0sah5TkKi8FSTlOXXZtjRtijJ5+QNhGqnKMmxtxP8tp4X6nn+cW2Ve7hOy2c/5cjvq643fZ1jWAn5TjswLtNADkjI1USCWUySFxVjteUo6qC1yerpWhS3TodTyKZIK8PfMgWWxe8WP2MUwID83vJXFxUKUc7pd9RPlj++h1++Xm+WTusLqfAAFMnrtUFeOnqLuNFKQcvdbotIW1IE147auq/W84sTZ4JCjl8Hy5PEb50W3b/VF9AdZmSaloE7fWKJ/VfIS0iFKOtK8aAiknTZtKg8YC1QHLbqunSDlqjsv2VdwvuCpB/D9vPnHvndFaheJUGnS7pMqVxvI2Q4YMq0JGysmQYR3wqmvpofEX3nX7aU5JhgwZMjwOoBdK40g5vHOzax4GeQF6PdRh5AOz9IL3LQb0A96p7ErvdpW/e9pg8+lQytELYjnoshs2QcpSu1mFfdVqlHI6rWznig/s6b4atSpNqhiChVsGYV8lAjf6u45SNioYZQ1eZByEFMVtSttXxdSBJNKg22+VxS7ouLbXaSUfE0xTN13aO33YVzFcW8NuyyzS+fp0ubsuSSkHWB0px7fbeV4FH12lHICk7K/4cWDrJcC5zzEEA0DljVpQTbujs7FIculllR+8GCmVcnLOzvzQPVlKOTH2VVIhgZVyfEEgTcop2fYhDL3bcdHswvQFyJjgccELxHX7UMopVijNy6eiljESfP+dFixljVilHEE8Ysh5FN+vz4JI2lelIeVsOs/cU6cFPU+Qtkv8vlglMlNr2RAItFKOVGxQu1Wrk2JHb0Apx2tfpUg5HCTjxfIkpRz9nkk5vKveCWKeuBs4dqc5vrFg7ufEfvu8FiknhX0VQH0Np9lnrXTiHmBWqFkFSTkV+7r8f1ApR1i7uZBKOY2FFPZVCTYQmpSj0tSq2XUuyeqAx4ON59Dr0nFDOOiLlAOqW27wl5VtQunndIWUcrpdo0Y1siFgX9UVSjkOCalds8tTKm24pJxCWSmreebAUv1IK+WE5g8eMh8H6Y+rwOmOlKScsqOUEwoWb9xLfWlrRSkl9UmEsJRyBiDlnLqPXqf2iHP2q5QjnmvHNpNC3MJR+l9aCPlQmTSkHFcpp9P0q1KFSKvlUUWgrVK59dSzdKtOhJi5Q5RH+aKYyyQp5XhIOZZSTgJRpjxhK+Vwew1h68VUJrVZqiP5grlGIYEAlBZlRwFNwlLKiRkv0kBv7hF2s/mEusTtIq4P23klsDVg0za6keZZgH9No9eJKr+59lWhNQm3LhdK9rHc/+24nAggPGZXJvpQymFSDs97+nhukWmJ2FexUo4gtqzMUPuYf8Q5jxpPpX2VzwqUSfQ5RcwJEUi11U6MqoeXlKPyYiVkXyVsiFglJmRf1Vw2asAuKSdJKYdJzSMbB1fK4X7EV7eO7yOFrLHNpCCZy9PzSqtuk9ncNR5NtBrx1xdJjkhSytH2VQm2m6uBtZ6mMJAiVIPSWxpNUMoRmzwq45R/jUXqCySJaWyL+U1eqi+lUWplFVXn2OayTYJfM/uqVSrl8Lys6RBqyuOClDNhl5NrX8Wfa/uqgFLOWttXndwPfPZNwL2fGv65M2TIYCEj5WTIsA6olhJ2MmTIkCFDhvTgxcY4pZy8WGjWkrJCdnmtwaSZvNhFWihHg6/9gOXlgeiCh158T/nwfzqUctbDvspS+uiXlCPSsio1o4yU40Wvu3pinLavypRyhgKXlCMXKrVSTt7UZ16UHWQhTNtXVcy149IVd51YpZwY+yrdTw6ilCN2jsaR9rop7KvqjlKOay/VadHicL7o70s6TQA5WkgNknLEImqcHL4+HwJKOZ7ACisChHaqv/TtwGtuoZ2qK9N2mgoV+ktbh3iXOwdmeZGyNmsrEgBR6yH3nqzAtIeQwRgVC8ulERNYdaFVZKRSjljY5d80lkiBA/CrgjzlZ4GXvM0OUKZVymFSztgWyut2I7zznedAbaV6IG1sSqMBoodPKUfMo7QCkGOLIo9r14WKjQcuKYdtOmUQl++pJtQySiN0/uYylZ9Oo6PYwPY93PdwHXLru0uabtfpGK9STlpSDttXuTvTVX37wE3AR4SCB5PY8kWPUs6CsuDKxdtX5YsmELvwKL2ObKR5o1unlk9R/vGctV1XeekErbmtLSqCAJdHoWTytTpFxJz6Qrx9lVTKSWVfVTXHu+h2A0o5NUHKcZRjXHA75f5s6UR/SjkyMNbrRPuKRPsqoZTD15NWCq1lAD1FytkYQ8oRKkz5oiCh1m2CyhUvo/6mNGIrNun+uegPommilVDKYeIu3zsA3PwXwJfeYo5l8E7+eaX8svFcf3644ICctq9ylXLU9xvPUX31SlQpJw34+OVT1N7TknK4nk0foH5BjkulERrbOe+SSDmyLo1uBiZ2AYvH6P8kYk9lgsgqgHhOFnZYgMe+qmg/ozKeehPwyo9S+XL7btfpHtm66PhdSrnEJRz2oZRT3SBIJmmUchapvSw8kmw5dcH11L/c83GjBqfJZENQyuE0AfGknFw+TJRNCz1ulGx1mTjwnGkkxr7qJX8H/NBf+78b2WTmb5ZSjirfTttDyhHjYrEcXpOQdlgAMLnb9AuAqa87r6KyXj6p7mk8eW7kErIHUSyRpB93/usj5Swdp/cLDimHx0HZ7lybOE2IrJjvQ/NjrXoXIBD0eiDCdUAph8tTqvwAilwhCBRSWYYhSTkPfRXY9T2GJMxIUrXj57PRLfHPat7fuko5nvI8ts8osBVK1H8e+SbwyO2UXsC/OY7zlcmIgL3OkMq+yiGwc/1YS1KOpZQzACmHibjFBOsrOZ4ygaQ+p+z0RN8mlc+Y4Av4SWan7gfe9kwzRy1U/BtIWivAxHbz/3raV936duCWv0x3nggpR9WZ0qhp/659lVbKUXnK9yM3dPhg2Vf12Y7SIM7yNkOGDENFRsrJkGEd8MvPv+B0JyFDhgwZHj9wFxt9sOyrpBd6YfhEEB/0NXNmYaNQNlYwg0Au0rgP+Z0+g828eLAaRZh+4bWvGvY1RNmeNqWc5trIyT7WwUEjV6a8H2iyxhp4tD8RwaQcbXEg1Vo6wtpKtStNyhlgIUzbV7GcdUwb4/P3Q8rhoH+sUs4qlJZ6XZP2JKWcRPsqZxe+DPrzOQolpZbiqetsAxW3eG8p5ZTjSVAhdQzAv1g7r3bjuwvyjFyO0s/qLdwvd1pGerwf+6rKhAgIrNB5GgtGKYcuClspR6VZBkBkYDpiXyUCEGMepRxfny6VcnihmhdRu13zm+YSLfgWKn7i09aLgO95hf1ZcSRdPW2rRfXRzRS44jwOgQlRUlkDoPR7lXJEIF/Fv+MAACAASURBVJ4hSTmS9OHCUsqJCZBzwMhSymna5KjxHfSqlXIq5neNRUUuVGnMOYoN3BY4X7RSjpNPEaUcFYiQdj68AF5OsK9y30fsq9Tr9AFbqYZJObuuBmYP2ovv9XkKYlcm/Yv13RblQS4nSDkqSDip2mrdUctZPgmgZ+6r3fCrGrFFDQdOZZBZ7wKeMko5WinFUxctpZxFE5AIIU4pp7lIdUySclrLVOZcRkn2VT6lHE1iiAloM9z5rNuOtH1VoD13RF/lI/dxsKgyQYSPlQT7KsAmerqkuKndpr/x2Ve5JFGdTlGmpaqZN7hKOd/+d+A//8ocyyiqYDpbGspd9XFwlXJcggPn2Ya9hkDpqgOlAfcn3GZSK+WoNn7qPrKbkv0qtyUOssVtJgEcxbYtpJSzeJTG0J7YYOJDZcJY87lKORww9JEnXUURgOrZ3mvt79sNowYEAMfuUGMzEzFcpRyY3wFRdUCASD9jW+Pvi8Fj1PxhAL1k+6qLvh94/pvoWFba02SyYSnlpCDlFEeSyz0Jeh1B2MUk5Vca+6o4jMbZV0GpGDIhi68p7auq4fmpXCMBqO+dPWi+5/GNiRT3KcUGnuunUspRbY/r9fF9wP5Phn8nIfvqEClHE1vypr+uzdhjNs/hpBqcby7VrhvrJ7avuu+zwFf/zL62VvUIWSgFbPI4rbUZpf7nfC/JFd2WUbaV0IqKy0Rc9c3/k0g5PDaObu7/OYzLvBpQymk3gFP32gpsG84GHvwy9Z1P+Vn6zLcGMafIjFNn+1VY0pBy3P5Zz0fWQtlXbHJjDETK4Wcy9du5w8BHXxd9PmOCOmDqQW3eVsMBgPGt5n2hGG8zdfhW4MRdRJgChFKOx75qYqc4b0mcdxhKOart+sr1v/4W+M+/jqqT+8DzMq4rWilnzPTB1Sm6Ry4nfuXxieeCifZV4vO1qF+aqJSRcjJkWGtkpJwMGdYBk9UhPfhlyJAhQwaPVLYHcpFYK+WoHV7rQUThazARCKAHsdwq7KvkAob7wNyvrc/pUMp5LNlXpXkA96HbVTulM3ulCDQpZzX2VartdJrrQ657vKPbVsQbj/0EK+XI3biuxHI/0KQcIZEeTBeXc+AYPhcvdJXH0ynl8ALeIKScbkekPYFQVIwh5XQ7FGR1d43Le+00FSmn4g8+dFr0XbESbgvthgmQhJQP5LFA2L7KtUycO0yBtDhLIoCCrp2GWaTsNGjhldOdBkzKYbWE1opZ/JSKBLmcnQ+8uFl0gkT6vuLsq0SwuFil8uw0o+WuCStCKYfvVZZbY5EWfl2VgjgUK+l2djNBa2wrBUq47oTAi95uEL8ybsgq1vmFJRJD9uEPfIHuf8810d/ycVyGIWw6jxb3d19N/7cbVF+ljdiEIuVYSjlMylmwFb+kYghgVFM46MWBax8xK1EpRy2G962Uw8FLh5QD0MI33xcrApz7bAA9CjIBNKfoNKgOVSbCqkZcTi4pZ2q3uZYEEyT4vlo1fz/AKk6alCPyjkk11UkK+ncapi75gt9SKaexSqUcJhtIUg7nZUQpJ9AHcltl5Q1JyvGRGFy4AXe3bLRSTmBskv2vtsETpBw+X3UynVIOYAK7gB1Qi6TdY18ld6vLfl8TrYpUn6UdCJ+j1yNlFx0Qd0g57TqpNRRH4oltElyOIaWckY3A034euOxGStegSjlcz9guKjUpR6Vv+SQFdX1p16ScfuyrtlAwstuiPHPtUVzI8YXHxpxDyvGNQZoMECBzykC7JOWsTKu5F1s4JSjllMcFkUsGclXfkqQmUxmnOnbiHvo/SSkHAJ7z68Dzfht48svpfz0nGpZSjuq74kg5/dqo+SDzLa2yENfftOS3yO83Re2rpOVdfc70Vz6lnEKMUk7XITAwKYfrTGOR7pnnBA99leYJAynlqDb4odcAH35N3B2Lc4i5lztXbdfoPl0rSgYTlQHbSqso5uMMTcoRSjlsX3XHe4Cb32rPa3nuErLa0eUUUMqpzdr9olRgkuMkPxtKaGL8Eo2R49sRQRr7qlye1vH6tq9yyH1u3Tp5L6V7uyDlcH983vOBzefT+7yHlDPzEM2ZRjcJUo60U0ujlMN1WpXvmtpXOaQ2QBFr+ryWftZUtlEPfBH41r8B0/fbx8lNHlwParOKeCPGDVknXEs0FzzOsuWiTymn16OxU56XN6Rw+lcLLtuGMzdePEZkrdpsND980Eo5TMphAv84qQK+6K3Ak15iq89GlHKa9m9D9lXNNbav4vlKRsrJkGHNkZFyMmRYB+RyOfz1T1x1upORIUOGDI8PpLGvspRyJEFmneyrLP919kwv0/uBSTkxSjmalJNWKadjv64HfPLXwyZWuPY7ff12CEo57m76DAbDIOXI3w1CDMlgQ9tXFc3/+jtVXnJhdjX2VS4pJ9YCKqV9FSullEZFoDdmgapfmz8Jy74qpv52WyYI4+tf6/MAenbAr+Ao5XTbxp5HLlx/4NW0YNpuOLsFPcFmuYiaZF8Vq5TDi+zi9/OHo8FHH5jcwmo5Oug7AClH2lfVPKSciFKOx5JLvveppDBkEIuVcoBo3ZKEFa1woRbs5aIpK+WkCfAzSimVcti+avMFtLjdWIxXyikyKacHi9icqJTjCSQBwH2fAfZc69+Nn8tTu6nNxSuO7LwS+J+PAFsvVffUoL/KuAnyTKqdsrxI7FPKce2rIEg5xUpKpRw5xtSpHCRprtWvfZVqUyGlHMa8soRjksw5z6bXE/vpVdopVSb8Qblu2wRBXPsq3tVeE0o5nVY0kBAiM7BSEadT5l15ggKfhZK5Lt+Hry7mC5ROVsopJ5By4oJbFilH5Tv3D1xGSQEcbtdTewDkgMXjhkiThkiXRMrhNHZbfqKptvpSRMt8yT6HtIhLS8rhwC5gW3lF0i5IOay6lS9RXTpyO/CWnaYO6XSWldqCUDsBaLxrLFIbec6vA9f/PnDxD5pr8U7+lRmbcJcEJn1w3XXJCLkc8KI/p36ElXJatf7JEFopJ0b9y/s7cZ0NzrhYdEg5cZtJAL9SDqDUcpLsq0Rd7Ucph/vYEHFLk3LqSg1or2mzlQlRt1ylHIeUU6wK+yhRhmx5kkQyYVL48bvoNUkpB6C68bzfAq5+pUqD5/qrQZxSDudDnHVjWuTzStUyFyXDhbDzycArPgCcd91g1xzdpFTPOjYJQFqiVV1SjrhXn/Upo+cQzDadS+MB20A1Fqhubb8M+LlPA6+9DXjt7ZQPxWqCUo4zn+X6O3+Y5mFpNt206iaf3XGn5ahwRUg5woZLEi416cZDcO4Iu9lC2YzNkoQOpFDKCfQRXC69rj13kfZVkkAhn3cYkoxRn7OtihhM8AspYbZWqD9PKkPvb9Xx3Le5pJ7j++h1xxXmM+6Pn/pz5jOfhfbsQ8ZOke9BPlc1l6HbcxIpRytkrQcpx7Gv6vfZnOtdQVkqacUnZ44h7ZC1YtKiTbwBjOoZoJ5R2RLNky62QmWVoiKTcsSxrRqAnhkHgeHYV938VrI17PXCberwN8T7W5PPGbKvKo9RO3vaTWqeKtSA+FVveGra5/BtlADsZ9G1UObOSDkZMqwbMlJOhgzrhKkRe0dGLyEQ2ev18BefvRf3HgtM/DJkyJDhiQq9UNqvUk7Rv0NmLWARUIZlXyUWt9yFrk6fwebToZQj5XY1KecMVMrJlwZP12qUOB7v6HXVwvJqlHKkmkhGylk1XPsql5jGgQDGakg53A9r+6oEYot89aUbMGNBecwsNLuEuC/+L+CdL6L3nT7Ji9Y1u4JQFJjD93qUNl6M9fWvHOwdcZRyuo5STr6ogpiiL7n308CDX1HqKGKR39cWWnV7kT/WvortUzxBORmQY8wdigYffeDA68qMbX3iLrzGwWdfxUGKkRilHE6va5/CiLOvkgHj4kiYlGMp5bDChXpuk/WwsTSAUk413Q5IDqRvfxK12eN3JdhXlek3EaWciYBSDpebeI7ledTcIeDE3WQT4kMuT2VSn7Pruw/FigjoNAyBi8lOLF+v7atEgLe+IKz2EFVsaNUUaYSVcpbMNSVc0jQHIlylnOJIdCe6hFXnEpRydiklAN4tzPZVZz+d5iKslCPtlIJKOUIlickx82xf5VHKkcE+Tcqpw0vKGdsCIBewrxo3dZuvuzwdPU6iNEJ52VpOVspJTcpRx604pBwf6VSC21llHJjcpYK381TOIaKCBJdlRd17xL5K5LkvgKIDt2Wqs9VJY1kFmEBZZYL6vPpcdPz0KuUIW9uQaoyllMO2iEpZbeYByvMZZa/GfXZekRA5b+QmiMVj9H7jOcCz3mAH01gpZ/mUQ6hMQJJ9lURpVNlXBepxHPj4xT5JOfI6IaWcVkqlHH6uzeXp+pO7VJqO2WolPmjVloJQMFHHcr/pta/ieUISKacBnLwP2HoxsFlZDVbGzdgcVMoRxASffdR4SlIO39+Bz1Ed5LzpByXP9VcDVjjwbRIaqlJO3gS/NRkugZSTywEXXh8/VsVhZBOAHpHhfPZVNY9SjiQgMQnv+F3Ae19lE5VdUgFbB7KFVWPRnHvvtcCWC819xJF9AI9Sjsz/nulPk86hbZKcuWqr7hBbXILtI560VEU7c0k5PZv0wH03j21MWgCErUyCUk6IlAM49TFnrplXZdtpqfHEqV9MjuT0SKsiRqJSzpJ6XhsZQClHHV8NKOWcuo/a9abzzWeXvBh48k/Z5NB8IboON/MQEcMAM3+T9aO5aAgnae2r8nk6l5zHf+vfgGP7PL/tAd9+d3rVE59NGY+v/UBbJVeontdDpBwxnkq7Ubk5BIiScrjP8pFnNCmHlXKUAk63Bcw+DNz7KfMcJecR0sJvUKWcr78NuOP/ozLm5zi3TR35BqVnZCNwKA0pR42x7TqtNWj7KofALxWNuG9xlXK0fVWgrln2VWtBymH1oEA/kyFDhqEhI+VkyLBOmKjaCwjfPRLPPF1qtPF/v3gAP/mOr69lsjJkyJDhsQetlBNzjAwy80I8yy6vhzqMlGbWi1VsXzUgEUYuEESUctRDZdoHVE3KOU1KOe5OymHBst/p89xSyWPQfOGFh8y+Koph2lcB4Z14GdKDLZl89lVdtq8Sj4tlZ+GoH/RlX8VKOSFSjkonB/Z4kReILlCd3A/MPKh+txqlnK5ZYAwRK7tCOcUl2jBkEJnBygSMTkupEQi1lG5XqcTMKvuqslCxSVDKSWNflS/5gzeuUk6vR4H5NEo5rDizcsos0hfKJliTBhyckfZVaZRyvPZVUinHo7bAsEg5QinHDajL8uZFVV7EtEg5i2uolKMC6SzZ31wczL6qPB5QX2HikSeQdN9n6P+LXui/FpNckpRydNpE8LfdtPNV21fNmvsoO0o5Oo2uUs4KBaK4/LVSjmthpoI133oX8N33KGJbVc0JhApeku2OzzItpJTz9J+nV0nKKSsSWnXKEBEiSjkBAhXfU3mc7p937ftIOWyVBZgAQojMUChRu2h6VIYqE6Zuc7+2pMgZ+UBdLI0YApIM8oSOBey+/R0vAG59u92fFkqUr/xZWvsqPm9phHbLzzyoVCA86k8+cFluU0pPMmjX7ZAtAquG+eaGUikHiJKutFLOBN1nr2tbLfgCdHJMadf8ZSp/w/ZVrNTTbZk+jAkdUhnsGb8M/Pi/qWuJ+QMH2nzWJkwyXZnuz1Kn5JJyYkiHZbavWgUpp1+lHHmdDXvs77R9lcrLRPsq9f3IJuo/pVKOq8TgQrZBqYABmHsa8ygUafJCiJTD9nE1svHYchFZCQHKToiDju5DOZNyauY8WilHKtOpQG4SUYbrzJFvAuc+J5mU4oNWzxqSfVWcUg6XwTCUcnIFk2ZNPl3jUA7Pg2oz/o1G0r6Ky77kkHI6DbLEufvDZHXJ8NlXATYpJ0Ri5rlR6Bk/opSjXpmsUZ+n8fYtu0kNLHSO8hjdb0QpZ8Umtsg2CwTsq8R83VIdzFHeNpfNvIJVzjQp55A5F48VSfZVbt3IF0y/6SMUaUu0krnfkH0VkzRDfTyn1YfmCvXRw1DKcX/PCmxybr/7auDGv3eIUI5NaadNRFxWyil4nquayzQfKI2GiRJ6rVFcvzhi8mL2YeCjrwO+8Y/R3x6+FfjILwP3ftJ/bhd6DUX0ucWKGsf7WGPU6qXlPpRyxPxXKu3yd9wn5ktiE44nTS4pRyvltIBvvB14z8+Y9IxusTc4rsa+qtulurIyYxOo3TZ1+BvAzquAs58BHE4Rj6sJkntr2cyfys78tlg15dRpAsiZNsnPrdq+ahleNFdMHqwlKSdTysmQYc2RkXIyZFgnTFbtB817jsYzT2tNmiwuNdZTxSBDhgwZHgPQO67jlHKEnLpepDhd9lWOUs7A9lVikcN9ENWL72ewUo5eQBPy10O3rxqGUs5q1Iwy+6oguh1Dyhm0DWRKOcNFt60UJgrmf0avY38HrNK+ylHKibWv6sRfx7WvkoEhd4GqsSSkoldpX6WVcgJ9S0cQGPIBIgzvppMkhXzBQ8op0cI/309rGbRreVbZV5VNenwL4NbO23J8mcUpKRScRfaVGco/tsOJAwd1lk+ZvJALrz4cuR1410sN6Y5tDLRSzlK8Uo4uY7avSqmUE7SvqtpBSYmOWIQvVumVyRJyDGgurp1STqdFebrpPEHCSlDK6TQUKUfMoSoB+yoZiGfkctQe7v8sBTI2X+C/Vi5PwbVeJ1kpBzDp7zRVsKAilHKUKgKrjvjsq7iv0ooN6rwcoNf2VeoZ3KeW1O0At/4D8IU/VGQGRVLgfOCgUux9SPsqDoQ5pJyznka7uC+7kQhnHJhYOWUC5+UxU58aLinHExiS9lX5PNU33rXP7bUu7KvYYgpwSDmB4LxlISDqw7WvBa57E71n9Qq9+zkQ/C5WDSmoX6Wc5WngkduAh2+xSTm5HB3LfWxq+yphSbbpHAo69kPK4Yq27RJ6lYQpDmqw0pNvbigDtwAFumWASJJydJ86bb73BWIlEbNVD6t1yLk4q24VVH3n/ofzWJOHSsDUbiJGAKbv7LaNUg7frwQH01dO9WdflS9QuXJexpE3tH1VzD0Hr6OscZZO0P9p+iy+JiOolJOWlKPqEo9BHPheSGNfpdqRJBPxscfupHnS1J7o79LaV00foPLbcqEhOEj7qkGVcnZcQUHXOEtAADjv+cArPwK8fh/w0x+IPzYE3/VXg0ocKcdj6TQopBKcfh2AlNQPRlU9WpmxCWFSKSfWvkqRr7mf3y8IB5pIqO5h6mwAOUHKWQiPC0m2QO0GpYfHnj3XAM/6NbLUA6gfmXmIiC+sROeCLaqK1eizJttaMri+T51F9diyr/Ip5Yh5B69JsSIkEFXKYcvIhkMgWDoJ/PEu4OH/Mp/H9RGcZst6i8l7JXNtTrNXbSdHVk+A377KpzIjwfZVq1HK4X7ZvUZtNh2Rkm1VGQtHaOzSSjlM4hXnbyiFn9DcCxBtRLTLYsWQh+58H73OH0EEx+6k18XjyekHwvZVQH/Pt201zy6UKT95jI1TypGkHFcppzRqSCiWfZXneW9BkXJ4TsrWmZ2m6nOUYg5Ac25uH6zU50tnGjTmqfxXZvzzLIDy4tHvkGLlnmto7Fs+FT2XhExLc9m2r5LQ9mgNoU7o2HHxfCFoX7Vi5lC+58TlU+ls+kLQ9lWZUk6GDGuNjJSTIcM6oViwm9tvf/DO2OMXmYwz5HhlhgwZMjzmwYG4uMVNKaduqdasl32V2Lmqd3eUTNBnEFj2Vc5Dd7+2LKdFKUfmyVrZVzlKH/1A2pwNXEaZfVUQLIe9mjYoy3cQC6IMNiL2VR6lHJ991SAqRRH7qhhCIC9MJSnlaPuq0Wjwi9FcNufpiiBlv+h2hA1KoH+QQctQP+JTynFJKl2WFBcy/bxgWJs1hAW9o9NTHnIRNV8C0AunOy4QrwMgKh0sm+3u/vNBKuVoi6wypTtEqtv/cdpRvXySxgwOVnBAoBlSygEFL96yC3jkW8K+ShIkpFJOWvuqapjwJe2rcjl7wV5aNTSWlFJO2iA/0ivlMEErXwC2KlJAnApAsazqWs+jlONZAO62VL8tCDwcSHrkW8A5z/Jbd/BxvKs4jVKOZV+l1KJ4J/6ECk5LQg23+caCKYN8EV6lHEnK0WovTp1npZyVaQrULJ9UVlWCNNdcMqpNwfuQdY7bYIGux3m16yrgJ95N97Bhj1DKOWmUIyoTYnFcBUsqk9HA0KFbySKk07J3iVenTFthsowk5cggA58vjqAnbWakqtZ5zwUu+xH7OhxcjbOv4mBtUl/iKuWcus9cQ/enqn4VK1GlHA44hsYcGTjdeC6wfIICRn0r5TyJXmWgh9MyudO+loRWEWOlnMmwUg4To1iJCLAJ7wxWuwFUmQaIAT77KlZq4LGUSU58vlDf2eua3e8TMSoKi8f6I+UAVJZplHJKq1DKAdRverBsC1P9RsG1deSxg/uctEo5nD/FCr1fPBq1R3HBgUo5LvKcpbEAbL8sXg0vlK9cL4/dQa9bLraVcnTQ0el3tVKOaF8++6jLfxT49fttAq0P+QJw3vMoj0NjThJ0nzAkUk6ZLcPiSDnDsK/ykXKGdA8h8FqLpZQj1DObgkjiI+WwTcuS6ufv+5RZq+h1nN9VaezgcaMeQ8oJqWIyuO1zHalMANf/niGmNhZMvywtHN1zlNScIWJftWL3p3wP49uJrCjtq6QKGrejiH2VS8pxCBI8N2g6Y8L0AZqPHxMxhThSDqfZ6tfYvkrUqZBSTi5H7V0r5XjsqySx2gdWBGKlnH42ZvFzG88l3fJPS8rJF2zCAt8PK+W46qCc7spEPClHbuxilKqU7l4PuOM99Nnc4ehvj99Fr8snktNPF6MX2Q+6z2ppwJanRbVxI2hfJZVyRLvMC4sqgOpWZTz6Hc8dvvJnwEf/O/UD7r0WK2YDCc9TZx5Q1xwX7aNI1xjdYghi/YDb/Mq0XZaSgHL0DpoPnX0N/QGkZhQHmWeNJWFf5TwvyI0uvKlC1rlul/qYfFERdzzrIJKU4ypGNZaAv7qS7LkGhfvckSFDhjVDRsrJkGGdcO6W6ALez/zzrTjntz6Bc37rE5Hvfv+jNDlrdtYheJwhQ4YMjyVceAPwgjfT7roQeEGi27EXf9ZNKUdcUyvlVPxe1mkhF4ZC9lWplXJUGtZTKcdaRFgj+yq5wNO3Uo76baG8CnslDvr3udj0RMBQ7KvE7zJSzurBpBxNNpFKOV1DZGSsSinHta+KU8pJsK/iPjSNfVVTKuX0SV60rtkVKj+B+qvly5mA6elfebFR7sJ3ra5YUlxKvDNhgkk5xQQJb0sppxg+Th8bCBzJXX18LJAuYFkep3Fv2bGvKpTDZcsB93ZdWRR0lY2AkvhuLtOiakEopQAAcsD0A3SPsw+Ze7UIEjJg5FoXiWWR6pRpEyURSIwo5bQMGQSwF+zlsc2lwZVy5DjSaQMn9pv/u11F4FJlxBZWLtlEggM+rn1VZSKglNPy51W7QYvabIvkgzx/GtUJTTLjHaRiR+zIJvV9j+4hlzPkmMaiHVTSig3q+q0a1VetlBOwr+JgjQzUsbKTZV+VoOhQ8JByAEqDL6C+YQ8wp3YEL08bq6PyuAnEWfZVDmnjw79Eyj7cbzAkqWR0E9X/miTleOyrWrVwEJlVO+JIEdUN1C55h3PoWKlI0q9SDqsbzB6k+ymNCQueqiEi8XgVt1sbsO2rmGxw7M4+SDmqvjEpTrYj7u9jlXKcvqo6aQeImORTnjDnWZSkHA7EigCdJHqy4lNc2ntdEyDi33IQVCvlMCnHY/sGKPuqY5ROX5nKchzdEv0+DqUxsaM+hnRYGiFC5KCkHB5T0lpXATb5dXyH/ztuX4mWQ6o8JGlpYhflq0+JQYLHF8saUxzL44OLtPZV932a0r9VkHIqQqVQc3LilHI8pJhczk8WWgtoNblh2VepPiaOlDMU+yqh/KIV4dZaKUfNr1emHfsqqbA3aY7Nl2z7tmKF+rblEwBy1I8cUqouehOMyLeN59r2VSG7z5BSziPfot/Jua8E9+f1edOnhZQ2WjUqt2LVY19Vc5RymJSzDZg8C1gQpJx2XREd88K+KoGUky9SnvP6CJNyXAIBj9+S3JBGKceXdm6PhZJpr776VR4z45FPKUcSq31oLqtNFGzh08ezZLtGaWLSR0QpZy6lUo6zDsfEDq2Uw/cgSTmLKZRy+PlPqtcocv3R79CzzegWUspx14c0KeckUsGrjuc8q0k0V+j5yIW2r1KqVtq+as4+LlYpJ2BfVSgK1SE1d7jno8C+DxCpuNe1CdkFMffg+S6nuTRqk9YAGoNmBiHlTJtXnme56oTHvkuvu68Gdn2P+mxfwnnFc0NTkHJcBThp8aZVb0Wd4zkity/fc1lzxdR197l06TiR9U7cE5/eOPA1QzZ5GTJkGBoyUk6GDKcRN98flsGL+y5DhgwZntAojwHPfmO8dLPcuWkp5eTW375qwx56mB3fTosBgxJh5MKQu3OrI3bEpkHX2a22HugJUo7cnTtMyED/oPZVhdIqlHJUOfQ64QDQExU9YV81aL17LNlXndhP8sdnAlo14CO/EpVfZvUXbV/l2L9FlHLUAtqqSDkJxBZAkHIS7Ks4cFWZNEoUbtCzsWTqSr/kRQnLviqklMNBy6JSKvD09RwEsOyrnGM7yoam5FHKqc9TsLRQFoSZkFKOWnx3pbEj6W6Ed6nrXX0qz3gBMBTAk8jlSC1nZdq2ryqWw2PVSRVwbzfEtdR9lMdosbE2Q0EgS70lZ4LxzRU7EMiQ7/OBwDJAi6hM+LCUcpy6Je2CAApG82ImKwoBtPDbXAoHmXwoKcUGWbb7PgD8/TONdQ3XZy677ZfRa6x9VYXO2fMo5bRWomOfe48A/W75JKXPt1taHsdIo5Sj7diUfVVR2FeNbIjaL+jgUk+oB5TMdbl+dJq2fQSXF38ekAAAIABJREFUkVuHc3mStpdjC9uSdduUZ82VZJsVyzJNXKM85g+WSaUcaV9VEepFFilHkXW6ikgxe5DKwy0rmeflccpDufNVjgfaviqFUk5cQDuXI8UDDhSG1BxKI6a99kvKOamIe2xDIgNxxaoJjpSc/q8bQ8rJqwASB+batfQkumIVQE6RcnJ20K7mknLilHLYvmoiaqtQGqNxhZVyWJEG8AfoJNGz5ditSMi5uFbdUuORVspxSDluf8BByF6Hgm0TDjGFIduCq3KWhPJoSvuqUWVfVRuQlKN+MwgpZ2p3lFzCdYjzMK1SjrRQnNihlHKS7KuYlCNtHcW4tiOBlBMk0An7qqf8LJUdWxZWRb8cp5TDKmGlIZNi+oWrnrVapLKvGoJSTj6//vZVXI9WZhylWzmuqzo3vg34HweMpR2gSDl1Gpv2PpPqwL3KwspXlzee45By+lDKmT8C/NMLgNveGW773D4sUk5IKadB5cYKIhIhUs7YVhr7pFJOuxG1h5MkBk3KEUqKhZJNzmBlFde+io9ZSkvKUfMWn/WWtq8qC6UcTz/LhIzyhJ+cLInVPrTU/CmkPhkHHsf4ecB9jkutlKMstE/dT3a5Mw9Rutki1bfZobmsVMHGqW42l4kEJhG0r6oDd76fzvv0n6e5BRNDAJrHMYFiaRWknDhbt1v/HviHZ0XnH0y6L6rngqB9lSC6lUZsMldEKUeQZ6RKYbdLY0hzCTj0dfp855NF+sWGDZ43TR+gVyZEAeacm85dHSmn1wEWHqX3k7vseRs/Y43voHsa2URklzjUZg3ZuLlM98BrExJF0UYi9lWClMPzbZ+CaUvVx+JI9LmU5798b4PAfe7IkCHDmiEj5WTIsI44+Ccvwn3/6we83/UcxnQxP6Asa4YMGTJksO1YtOS3Cj6vNylnzzOA3z5CwathWCMBHqWcjv/zYPpOg1KOzvdcdCflMK/BD+yDknLyxcFJI1YZ9bHY9ERAr0ftL18YvNwfS/ZVn/994BNvPN2pIJy4G/j2vwMPf83+vNs2ZcL/6+86KhAgA/dDUMpJshKR34WCqPz95G7gxreT/QFAi2c+pRwmZ3YGJOX0eqpvKdr3EkmXCFq66jeM2pxalBZBA1dVR0uKV8396CBtjxbjrd11nrYgF1HdPF88Dnzg1WbhLU5VQO/qc6z50u7+Ht1MwX9tX1UyEuWRNDeBmQfNdVxiTXmUSBErs3bgEQCQM4uRLUHKCamWxNlXFaumrhcrZvf3qfvt37DSFEMG07X6xiiwqBZH+1HKYQJcU5B75h6mPpAXzNtOMH+7ss+JC3gWSoqU04Vld8PBRXdXpmuJBND8iheofbul9XF9KuXkcsbajIMFvBBfnTKBHL5fGRSyApVucBi2fVWcUg4TVXhXfbEi2k9HLYYn2VfJeibqnwxmSGzYYwKFy6eMfVV5zJaRz5fsoEdzicg8vQ7VfZ99FUD5li/Q/3VHKYeJO03ZFwQId6wCEkf6AoyFVdyxMo+S7KsKimjFASVWygGAR78dJeVwQNANwLdqwPG7o+dv1UzdYgsLIL1SzpU/DrzyI2TZVJmwgyic32xflUYpx9213RBWLpUJIuh4lXLkrvmSPd6F+nepKsqqW4WiUsqp2fegrVicMnWVcoKkHDFm9G1fNSosCePsq0aMfdUgZIhBlHLyeWrnU2dHv+M2w4H6JNsl/n7UR8pJaV8VVMoJqMtq0kBoDqA+r24Arvtdej++FXj5u4GrXkHXkHZBPqUcPodWylljQkkIWilnWPZVKUg5Q1HKKQilnGL4msNEZYKuVZuJKg7LYxjuGF+s0u8Wj1G/uveZwEM303c9D4Fh4zlUz1s1u89z4SMf3PMxZZ93LIVSzkJUKWfhUROIB5S62Iix4FqZAf7zr5W1TIxSztRuIvZadpAOKcdVyum0FWlbkBk4fzaeo8b4njlnaZTe81zFIuUI8pSLktMG6UCVJlWnymMmH3xtlOc+ITK2VAHxgcktcQSSENo1m1w9KCmHlXI+92bgX18MHLwF2LjXPOv6FGcaS4oYolQKv/424J9vsEkLWilHEvyVUs6Ju8nekhW+54WF1fwho4iY1r5KL6HIOW7M8+DMgzQunnTUUzoNYxtlKeXMeo5TZcY2ZoB6npOknFHzXV6o6HSaZAnL858Dn6dXVqEBhFKOsK9ipRxJyuFzbjyXVKlCdW3uMM0PXUhCFCtUTu6y1QlrM1TWfK3x7elIOWyR11SbR3z1UT63d1qO6m3DzMVZmdKnlNOqGdtul2jFRENJ3O4X/NzRaQ5m950hQ4bUyEg5GTKsM8pFf7OrtewA4E3PokWhicoa+wVnyJAhw+MRUvmBH5TZC31QUkw/6DqLp1J2emClnIb/PSAUIPpUyjld9lV6wWjYpBypZjEIKSenymiVSjlA9iDrQttXrUKtStbXM52U01jsbxfgWoJtm1wiBJMKJImR0euoPnNI9lVSrj6pH06yr+oKAt2TX2523ZdGo0FPXtDqNM15+ybluNZbgfqrlXLKYQJmbS6qGiIDqIAKjpZs6Xy5MLd0zNld5+RTr2cWsGW6udwevgW4831k0QKEgxhAdAGcX9MGPMe2kPqHDuhWwqScmQdNHWw3BAGIlVEUSWFlOqq0kMsJlZoVs6BeDJFyYuyrSiOClFOlBdvJs4DDt9q/cUkQUtqeFzXHt5kdi0mKIBK8sG3t3lS7aFmFR9c3dY/aviomaM07Yns9O0Csr+csAHdbfqUcbku8cOxDv0o5nL5206h2jGyk1/KEUMrhHbuSlCPmWq6NCkD1lfOFAyCuzVeuYPqPS39YXatqkxbTKOXwdXIFu36URv2WEEz6OraP8lvbVwnlpfo8BRVzOVOPGouGxLYyE7av4rKtTtlBpJVpqtv5kkPKCQSRtVJOEilHWJqFCGIyqJnULnI5YwMBkFLO1kvp/eKjdjBYtndWu+IyuOUvabe4DL4Cdl85ssEQ/tKScioTwHnPpffScgwwwS3ehe8bezoN0NyzaM7XWDQBVqkakcsRSUPugvYq5SjLNSbbJJFydF9bNr/l+QvfA7d5H5kNUAH4o+mUcqQSTBrINhdrXzUKoBdfj+Mg60E/KI0AG/Z6Pq9SH8bllZaUI/NnchcF3pm8FbIt0ko5IhCo60TOkDZdcDA3NAcY20Jt6fv+yB53L32xUfUqjSBWKUf32Wz1dZrWNzVRb0jX1+3SR8pR+TAUpZyCGYfXSyknl6O+cGVGkOpdUk4M0ZjLfOk41aGN5xhrJ30+h5QDkDpGux4mMXMblc/Yd3+EXmuz4bbvU8phIvd7fgb45K+bY1uqzhar1O7u/SSROE7uj55fK+Uo+yrAtHdJSOP5RsEh5fB4Ie2rGDuupO/rc+Y4JhD47KuYcOAjDscp5fAYuf1y4Oh37fuS4LlEaN4Xt1EAEPZVAbvhOLQUuZR/a6lHN2henKbf5jWehUdpvvfot2wyria3OEo5lQkzNh+9g8ZDaQm18AjlmSScFiuU7rnDNM9j0sacIOWwddWWi4aklOPJ+8Xj9rUY2iqZlXICpJy2o6bK9StfcpRyRg3Bv1C0n1HZmhgwpJydV4n0l6P2VaycJe2rpFIOesYq1cUX/hD4lx+KPtNIUg6ff3I3EZJ4zuUSvMa3xZNyej36zQZFzG0uUd/iUwT02Vdp9ZwWPWPwNQG/Uk5zhdqBbyOSVsp5JPq7tJCbQerzwKkDg6kSZciQIREZKSdDhjMExxfsCdTUKE04xjJSToYMGTL0Dy3J3hEB3IKRCl5rhCSE88X+rz/zoFJ5kKQcjwSt7/MQOFi8HgQlhpUnOfuzYV5DK+X0eW+aNFLo/7cMi5Tj2RH9REa3YwhZg9Y7+btBiCHriXY9rPSy3giR9jotpSCm5poyf7sdFQjw2VcNcF+6/ReS1aj4/En2Va5VhLtrjG1w+FzyvCFiTVzak6y3LPuqAAHTt5vTVdXpKMWCkghES3JGfd7sbgSiUvHdNqUxQspR6dMLr2rxLo1SDudjyyHKJGF0C+3o5d9riXJP2UoFDK9SjmNfZUEEO5Psq/KlaHDUXdxmFRa+z7OfBhz5pv0b1y5IknJ4oXR8uyDl9KGU41Ou4R3RvGiriUeqHoxtIfuInVeGz1soU7vg8U6m3b0eQIS+OAJTrH2VCLilDXIXyjR29joUxHr6LwA/9R5q664dUaFsy+gDqjx8SjkjJp+0Uk6MWtJlN9Iu3p1XCjJei9Lms22Q0DvjnTZy/e8Bz3pD9Hgm5TxyO71yQN61r2KSiI+U05inNmPZVzEpRwVQqhuMLQBAQb2xLbYiT6xSjgrEJVnPSKWcUPBb5k0aslpJqYY1l2ln+YU3mO9cpRz9G0cp5/Cthjgi4SofsIVVWlKOhOwDAJPfWilHBFD+6Qbg5r8wgRnuk6qT1LfIvl/m0cTOlEo5baHYlZKUUxCBMW1f5SrlBKz/EpVyRBr6Vcop90PK4eulsFiM/F7Vg36UcgDgJX8HfO+v+r+b2GWIlInqJqyUI/JnYgeAHpFxgTCxh9Ms+2Tu0zadF1b4KlbVvCxA8hjZAPzmQeDqV4aTXRqLV8pxVTpOFymH6+Cw7KvSKOUkkTjTIJc3xIm8M+atJUY3KaUcMV+Q9SSuj5Sk1/FtNC7UZmgOqa1+RL5tPo9eeRwMzZe4LnGftXDU2OHUZsIk80KRysuyr1KvMw+asZTPXVJzhk7D9IEr08qCyaeUszVKumjXo6Q3WW5yTUorgYi6yfY+c4fMuMJWO9q+ShA5eI666+ro/XOafWnnNO36HtNXxSnljIWUclTZhJ7d2L5qUKUcSa6Wz3pcPv0o5SyfNP3sJkHKce2rul2jkMjjO9tNSVLO8buATefb+cv2QvNHFClHkTbmj9i/A2juvnwinZKwHvPlHDegIASYsePYPvtzVqRkpRxtX6Xy8xvvIHVB9xmxLAjPFvF8xFbKkSqtrDQ6upnyPl80lruA2bDRWjHPIbxOYCnlqPLZpPqL2QBZZPYhIrLd9UH7c4uUw0o5uwH0zHVXHJWbJKWcxgKldUqQckJKObKNdFpGIQhQ5DIm5QilnOaK2VwFUH0sjak1AoeUw8/1C0cHV6WWz4GNBeDDrwE+/vrBzpUhQ4ZYZKScDBnOEDx40l4EbbVpEG121iF4nCFDhgyPN+TFIrElu3wa7Ksk5A7zNFg6Afzt04D9H7cD6hHFiz6DzVopZz1JOfxwKO2rhnwNJhJY10sJXnRcjVKOJGH0q8bxeEeva5RXBm2D8ndnulJOuz4YeWUtECLtdTvG1g+w+yZWypELs7wQN0jeu/1wrH2VOrYTOEZaEkqURu2gp1xY6rTs9ukSWeLQda63Gvuq+lyUoJAvRQln+aLZZQnYgV7A2V3njAda9cC1r1LpcyXKOSjsQ0Hs6gPMAmBqUs5mWgSV1ifFCuWNO1adFDsp2w1zLV7kLo+SysXSiah9lVyctuyrxH1pkpLnXl3JeV5Y5t+c9XSSnF8QAf1uy7GvEiQKXlgd22re92NfpYkXog6zXQEHTXz3+KqPxQdPmRDlknJilXJc+yqR17H2VRykzZPqSxoUK6bdFkoUkD7/OvrfVcrJ5YwaiiblFKPBYf6NVspR5y869UDmx4a9wC98GTjnWYK0yEo5Ke2r3MDgBdcDe6+NHs8KG3e+j16ZlFMeo6BCr0dEOq4/HKyUpByA2oXPvooJXuNb7cDC8kkizVUmBCmnEW7bTLYYhn1VP0o5gFHKmT5A/+9+igmoycBHyUPKcdOwcsr+3yXlbFwNKWccEfuq4ohRiuLxqduh4POxfdQeZV3h/KgvUJkeuc0EewFjZ8TwKuWo8UeTEwOqMRFSTkX8Vn3G4wT34S6hgfvOlRn6zcRO/7VWQ8qxVLHiSDkj/veprzMgKefSFwNbL/J/NynyI4mUo1U3pH2V+j0HckPkmcmdwE+9D7jiZeJ86tgdl4evKecSIbh9pYvyKOKVcljt7gxRykkiFqYF960+opS2rxqCUk7Oo5QTUkwaJkY2kV2onC/0q5QD0DyB6/HiUZukz9j2JPr/4H+qc4fsq1gpRfWl+z8OoEcqNdwHhfKc1eKYdFCbJZJybcYmOrbYKqlqkxVWpmlO7iO2jG0DNp9P76cVAUHa/vjmnz5idD5EylHjyqSyyNKknONmvePIN+get3j6It0GffZV6prSTshXv7R9VWDeVyjSPfmeEbtdRWoeX4VSzohSzlMqou99FXDbO80Ylabf5jWe5ZNkv/eCN9tz5oLzXMXz7fI4lVF93sy7ZgQp58TdUTWyUlVZLNWIsDGykeaP0r7q+D6ac2w8h64prSuDEMrTDB5Tlz1qO1opxyXlSKWchnq2zlF+NldIPeprf6M2eYj27CPeAI5STlnYVymlnOoG4Jxn02fjO+yxkefoy878DDDWYYBtXwXYc2AJHi9v/xf785Vp6Ho/e5Dq64QiwMgNK3Ljx8R2ysPQuiLXP03KWVbEHo9SjmVf1VCkKFHnfPZV77gO+PJbzDmaK8K+KqCU02mY9/2CbeYAVd8fsgloGTJkGBoyUk6GDKcB110Sncje9K+3Wf+3FBmn1lzHgGmGDBkyPF4g7ViklVQuP7gKSj/wLThxGvohfCwdp8Vx9kln+ILrjDTB5tNhXyUDB2tmX9VLDpwHfyuVcgYkjcjg+JliXXSmQOfvKtSqLPuqM5z01Kqtsz1cDLQdlM++SlhUyXLpdu3vgCHZVymlnDjyYCi97vcRUs6IrVBlkXKazk6zPtqnSwIKjSFaKYdJOR5Sllcpp+AQhpRSTlHtguv1ogu1rGgA2DLrgAjEJijl8IJdXCDelUTn17QBz7HNVA58Tbkr0B2rkpRySmPA8TtpkfDc5zgXckg5nQZdSy5aF4pRSyH9c94xXKLyKI/Zx579dHo98g3zm07bsa+aFEo5K3R9Gdiv9BHkLws1FIa2r1J1V9qlpUVB7fzude280co8DvmLg1MSWgFgzPzOBz6uOhVVtQqhWAkr2bikHPkZt81CEV6lnNKIWfgOnV/2dXJBns/daasd0yntq9IGY8c2A8/+dQrqAMK+apzKqV0PKOXM2wvlS8cDSjnq+A176RiuP8vTRBorj5l61q6HVVWGaV8lFSvSKJoUK5Q2Ju5tvdjYnfiUcvIlc223vbtBn7VWyhnZELXcWD5J40h9LkqK5H6isQh86jeoL7nuTeb7iR30TMABoiAppyVIlIE85t9oBTSfUg6TcgL2Vdxu2C4hpJQj65UvWBUHqfISV/9Ol1JOHCb6IOWMbACQM8E9wJD2uK3HkTEu+j47r4oV6qfPenr4N6Ob+i8PF6WReKUcrR6m8jdRMWiNoMeLIRFaYpVy2L5qAHKYi3zBtLv1sq8C/Eo5PiKJD65d3aSPlOPYhm65CHiYSTkJSjncZ93xXmDrJaRmGKeUA1Cf3nDsq9j+afmE6ePaDTVnYAURVso55VHKUeU8vo3G05GNwMl7zXl03WeFv1L0t/J+5fc7rqDXucNmXJnYQe9ZObHTMM8HR24Ddj/VP9/ivtGy3oJ9TSYBAX7iXJJ9FZ/f93zF40l5lUo5AP2+Pke2ZQc+3x8pJ5enZ5JOk+7j2W90FFscu1+eL5bVfLcnNvtxn9xcJuLCNnEeTicrs2w4m8p7w9kOKecuuj4T3EMWVp/9XeBjSq3E135c6zRGp22eHY7vs4kl7aZ6JhPtZWInPQcw2YXVlyylHCbelOz6Wh41881C0d4Qcup+at9MEJ3YQSSdQsVsAMoXbRIUYOZyLmltbAsd47NVajdpjjS+g8jPn/8D4P03UXtfmTE2UwuPUHqrYs4FqOd0MSaOb6d2Ju1fJTQpR819m8v0Wax9Vd0QsmWdc+2rlo4DJ+8BHv2OOkZtMCqN+tuaVAIa1MKquWTI9UvHqd+bPxJdazidaK+CdJQhwxmEjJSTIcNpwK/dENhJI8AKObVWB53usKUEMmTIkOFxDq9SDpNyhtynfvAXgM/8jv1ZrH1VH6QcfgBsLtsBap8Njf4uxSLH6SDlWDt71sq+qmMebgcl5eRXYa/UbzmsFh97PXDrP679dYYBi5TzRLCvapw5pBytlOOxOWISAv/P6HWMchRjaPZVCYphUvnL+33bnEvC3TUmFQvaDUfJqg+lnLT2VZosxKQcTz2vzRnVBIbXvqpkFqA7zaiCSbEc3dHJcJVyIqQcligXSjmhIAYHEphA00oI8rpgggErO8hdgfV5E7wA6D0H2tsNcR+sVvMUYPsVwM99Erj8pfZ1ZHCjuUKLh740cqDFBdclLfE/ai9C77iS0n1YkHK6rah9VWuZyp13MpYFaaUvpRwPSYYX1iP2VX0En4tlVQ966ZRylk9GrQqkXUMcNCknpXUVQHnMC+PufXEwqRBDygkq5VTNLm5e9I/YV4mgoyRk8LlbK9ROB1XKicMLfpeUeV7wZhOIk2pJkpTDdldHv0uBE63CsmLfk2tfxcH9uUPUvhrzhpTTVPW20wyTiSqTKh/7UMoJKWJwucWRutzjW3Ui7uUKZF3gJeWw1Z1HWeWC6+nVJeW063agkm0RBiHllMdtMujJeymgw3nKgUnuD2uz1B59SjnfeTdw14eA5/4GkZAYEzspQMn9OD/TeO2rVB8aIgZopRzVrxfKSrmtZfr6+jyRaDvN6JwAMO1Gk3ISlHKqG/zEyDhIsk2sfZVjHdIviqeZlHPuc4HX3W4UNwBVH3OkNJDmHBKFEvArXweu+cXwMc/6NeBVH01/Th9KY0illDO2mdpVyIJrrcFta5C64cPkbuDsZwA7nhz9bphKOde8Brj2V+i9VspZh1DO6CYKfHY7flJO3JxG9mnj2wxZc+FRQdJ37mHHFaYfSVTKqQOPfIuI0k/5WaXqM0N9WaJSjpr7tpaN1VSva+ZYfA4mg3Jfu3gMQM8h5ajNCyObqF5vvUSQcupmvqIJo459lU6bQ8opj9PYUR4H5h6mcaU0Rv1nr0vEDiZwLp1Utkp3A2c9zX/vsfZVBZOGzRfan0kk2VcBRh3TBY99pdFVKOUIUs7xuwH0iCzAlj1plXIYPsUfTZpQ82tWEaxM2ESxiZ1GKefkfkrLtkudc4l6yETLqbOEvVmD5nDbLjXzaSaJtWrAv/4QcETZud3/WWD/J+i9b41xdDON39x+7ngf/Xb5hErbZVTvJWmn06T6JpXQNqp54tHv0iurE/rsq/hZV9/vCJFuNp5D9VY+e566j0g52xUpZ3IntZeJ7eJZVaSD5zx8rYteSHa2/H8uRwRqn33VwiN0z898LaX7lrcC+94PPPglqpsbz1Hp7tnl2hAbVlz7KsAQ4VwwOWRsG7X3+jz9+ciuUuG2rfI/lzMqpq591dE76HVOWW257chtQzVBVHGtWtOiuWzm8WzVhp5NJmN8/R+AE/vtz07eZ1vlrgW+9Bbg77+3PyvyDBnOQGSknAwZTgMu3z2Fg3/yIvzWD1yCStHfDJttM8CsNM+QoE6GDBkyPFbgVcop0gLQsIkgR+8wO5wZPr9nTpcMvh6/G7jz/eFzS1KOJHlElHL6DDbrYHFv/R5oLKUcZyflMK+hrSb6JH5YSjlDIOVIxY61woHPm12FZzokyWMoSjlnun1V7cyxr+rGkXKkfZWo92wF57Ov6sf6iWG1/4Q2xuXc6/j7J/7Mq5SzbP5vived1uCkOc6XQkLfopVLivTnIx7VZqP2VRxA1ddTpBwdyK3RgrsMEhfKZgEzQspx1GwS7ati5P4LYleffE0b1GIbDl4oljZCt/wl8I/PUapSXdpJueNKcx1XKefZbwR+6RZg7zM9F3Ltq+p+UoS8vgTXc86H8rj9+2KZpP0tUk7bDhBXhLpNq6ak3EVgKc7qwYVLkum0zWKrtq8SlmBpEbKv4nQ2HVLO0olo4ELaNcSBj3PrexyKZUNEcoPvPqUcHRxQ5ZcvIaiUA5i8cq35+DOAFuPld5wOno8lKeVwgKNfpY6dT6Y6ztfme2su2qScyV2kfHHn+2mhXgbh4uyrONgy+7AJmo1tVkSS5Wh7c5HLUaAgrVJOvhQOvnM7S2Ndxce3axQg2rCH0hinlCNJHBM7gStfDlz/B1TGrr2Dq3yw51oKrG5zrCjSQKplnbyPAsaX3SiCkKr/ZKuU2pxS9ZJkKtVP3PJWYOdVwPe+3r4GK9FouxWPlYVWynGIjS40KUdY4WlCDwd7ekTg6rbiCY2H/stOnwuuV/1aVwF2+cRZH1n2VQOQIdZCKacv+6qcTcgB6D427jXPFP0qpGzYE09kGtkQvWa/KI8mKOWosrjmNcBNn1/dtVaDy14K/MR/GLuS1aJUBW76DBGGXWhFuSEQgC59MfCkl9B7Pdatl33VtLEfBpx5QxwpR7S/sa2OfZXYLCXBhFQgPDZwu547BNz6DzR+XfVTRCCqz6m5VwyxlEk5TK49eY/5fvEo9X3dNpVbsULzJR772RJHzn/HtwFbLjQEoy0XGdVHSQ7nubSllBNjXzWyUSmr7KUxu7FAx/D40K4bu6TlE6QI0uuSYpA335iUI/PGsa8CjIXVIPZVAJFLlj3khaZQQJHPNmnRrtv3cPwuej9/pE+lHHFfPnKRfq5Sz0s8H2X7KoDy64LraT7S6xniwnZHKUe2fVZnmTrb1KPpA1RmWy8xaWHix4m7gYe+Ctz/GXo+mnmI8rU2a9vBM/J5amMLj9L3H38DzSF4nnDBC+hVWlixfZUkujN5++h37HuxiMOslKNI8Pmisau65EXAr36X6nl5jL7b/3FSXNlyoSHlcH8wvsPkuUXKuYReuc7tugr4wT+z5zkbz/XbV3H+7rgCePXngV+8mfLq1P3Un41uMYQZl5TT7VB7H3WUcgDb/lWC69/oJsobJgX5lHKkJXSnKfqFMvUX2r5KtTEmR80donrAmzK0fdUKcO+ngL+7xqjHMHF/EKWcXo+abIg/AAAgAElEQVTSMMGkHLG+zcQgRn0e+PRvAv/xMpMHjSXg7c8DvvKn/V+7Hxz5JrD4KKnnrhdOHYgqUWXIsEpkpJwMGU4jXvPc87H/j17o/e4r95kFo+VGZmGVIUOGDH2BFzq63ah91aAqKCG0lqPBd3lNCde25bZ/Bj7xxvC5eSGotWICcPlSglJOH/ZVwPrYeQH2IoJetB0yIag7DKWcPi3GJCz7qnVQypH14kzBA18Ebv9X+zMue20hNyAZS9bVM10pp1X32xe5uP1fgHe+aG3Twv2DJNN0uwCU3ZsmsrlKOQU7ALYa+ypuj9q+Ko6UI8lBnjzUijRO/1qs2v2fVBnpNFevlJNP6Fv4/FopxyHltOoUWHZJChGlnKaydRES741FWnDWEvcVoWKTpJTjLDKzlRSTPGKVcngBsWmfO23AkwP0p+43aeF0H/o6nW/uELBwhPJGk3IaQpUnRVBLxv5bK/Zip3U/VX+A0g2gfc9PA8//n/YxZz2VFki5fnbaTvtgdZslmheURm0lkL6UchySzIpQ99D2VYOQcipUli4pR5OAHPuqpRNRqwJud3GBGWD1SjkR+6rR6OcRpZyC2NEvlXLcwFgMMcvd3crnZjJbOUEpR++MH8A+R0LXp2WblAMQ0ePE3dRvnPVUkVaffZU6j1bKedgQU8a2GnWXJAIHQIv9SfWYd2zH2gwx+S0lKYeVcmYfNmScWFKO6DMKReClb6cd3KOb7bYE0Hld+6pfudVW/EmLyripv99+F42hT/5JZT9TNsQKXthn+yqfUk5lEnjZO6P9lQxuA/5NAGxBlUjKUb+RCmhM6JHE8tocnS/v6Ts3nUsksWN3UtsbD5FyWC1li//7OKS1r7Ksm84QUs6EqEeDqptsESrbcfZVpwvVDSK/fUo5qn5XJoCtyYrha4bKOAWN1wPDVMqR0Eo562Rf1W3R2BcZV3O2EqALHgdzeep3q1M0hi8cNYFld4zceaV5HyL8TO6m/uaLfwTs+wBw1Svo3CMbqS9cOhGvlLN0guaarIgmVR4Wj9mWf4WKsq9ySDlyvHjObwA3fc78v/USCvwvn7IJ73yv+QRSDvdv/Jyw8Rxg9iAFuyvjdr4wCWTpOHBY2QztFvMBCakCqa/PpBwxj2VSjs8CS5NyYkhtY1ujxFfABO3LQimnL/sqRymHy2nlFNUpYDhKOWzbzM/Lmkw0Zspoy4WkblOfJxLE8bvpOYXnJAyt3Ddh5sFTZ1GamytKYQekCsMkd867aUU2OXUfkQ84PSfvC6txT50FzD9Cda+5SIpNTCQ5/zp6PSZIDKzK6lPKedQl5QSUcvi15CGrl8eIDP3gl819Tp1F89cLv58+s5RyRNtg1aG4+famc2lO6K4nsKLL1NlEzNl5Jd3XyXsVKWezIQdXBNmqvqDaei9qXwUkk3JGNlJ6WQnJVx+LDilH3ru0rxrdTOXLpJhOk+Z8WilnjNpRu06b807up2fp2iyw9VL6bVoCiWVpVqf6xfPf44KUM3vQ/h33h3OHgA//Mp3n/s/S86+7WfVdNwJf/pN06UmTXiaXPfTV4Zxz8Tjw3leZsvThvT8DfPJ/pD9nq54p+WRIREbKyZDhNCPn7CBrtDtotDt46JTZ3bucKeVkyJAhQ3/gh+5e12NfNeQJcqsmvKcXgfs/F2Nf5di2tOrxAW6tlLOkHshz9AAZUbwQD6T9knLWzWJHSuw7i7ZDu4RQyhmElJMfolLOethXNVcGUy1ZS9z2TuDmv7A/s1RSVkGMs+r5OuRvpzU4gahdt9VPQnj0O8azfa3AbVz2DZLYIpXF9Pcd+ztgdfZVXHa5nCK+xeSNPL+vf4wl5Yh6Ia14Ok0qD17wbvexQ1PbVyWQcrRSTtlPyqkrOWevfZX6bbdD5y+UbVJOc4kWdnmhviiC3m6f33ICsayeEbKvaq2Egxh6AbHuP3cStl9GC7aHvq7SItLNO11nDwLTD5jj+XpJyh0WpFJOTQW6PcHbkFKOS8rZey3w9J+3j9l8PvW3HAzvtuxgRkQpZ8S0GUmySgNpXQTYQY7V2Ffxwm+vByvPKoJQJK/TXIzaVGn7qrVQyqmYe46QcjxKORwI4HJgKXhKgDjviPke8NcN7utcJQ8+N7ebRPsqx65iUHCZsCVHRZByWDUBsEk5Pvsqrkvj2ynIOHuQFqABYV+VkpTzkr8DXvQX4e8Byv/JXfEWRVwe/SrlzB0yQSPecc3/83FAuIzGtkTtq1ylnNWACU6dFvDd/0eWC6zKwQEUwOxer89TXyXLbcMeshD5kbeZwLGEq5Tje95gC6okEqVPKSdfpPS36mb3Ptts+QiNlQng1Z8DfuErwCveF1aSWpVSziD2VWcKKUeQlAYl5bClzGrOsZa4/veAG5WVLve/X/8H4Ev/27adeSJhmEo5EtKqca3Bgenl6ah9VWXCT9xgyPaeLyirmp1K3WAf9TUbz7V/s0OQckLkz3wBeOWHSakklzfWbJzW+lx4TlSdMvO3TerarlKOnuMqpZwkUk6xbKeVSWcn99PY7c5bkpRyeNzkPkiTchaiFkqs5rZ0klTZtlwcnm/pdHiUcuRYvfsp5v5d8Hw2zr7KN8YCtC5QHCHS0CBKOTynBqL9yfF9NH9Lo0aZpJQDKNWSBhHYeT4qyRvbLgU2KXWxmQeAE3cB2y7xPJOq9G7YY/pFqWJy8l6qA5svUONiTpBylG3UqQOkksM4dW94jXFyF52X1WNmHjQEkS0X0bX5uQugZwgmnzGYvH3sTkqXz46V6wHX5UKAlAOQ7d4VP07vt11K+fCyfwEuVHaiV74cuPpV9vkAIpYA4fMCVN+7LbKxk+B2yptC+NgT9xDB2CLlCPWpxoKxorLsq9SzTpCUI57ty+M0TwUS7Ksaau7HeVihz5h0Ux6nP7kGMvewIOWMUN60akTE4u9Xpqlej+8gslpj0ZDWfDixH/iTPcB9n1V5IJR6cgVgWm2oyZeIACXB+Xz5jwL3fhK464PA3R+mz04dENe4hzbsfec/hqOQvvCo6ZMfunn15wOI3HP3hymdPvR61J6O3ZHufO0G8FdXAN98B/1/+78AH/j52J9keGLiDJzRZ8jwxMbFb/o0Ln7Tp63PlhsZKSdDhgwZ+gI/qFr2VUy4WENSzr4PAO/+MfPgFiHlFO3AdzstKWfF7KZ1A8+AowCRgqxwOkg5/CCWy4nyGTYpp5OsZhH8rVTKGdReaR3tq7rdM8siiSHbA0MSMlZDjLNIOWuslNPtAm+7NkowSoNOS/U9KcqmpYhVa7mbhuuIl5QjlXJE/mqlHGnjogJ2g1iHWfL3Cf2wpRrjyUPuQyP2VVVbocq1r+q2zGJiP/eg7avSknKKfjUgVlCQiheATeDR5yiZQGpLKeVUJsxCoWVf5eRRUCmHyaNK8WNlls5dnwvvfOXF2o5QysmX0lsnFCukUMHqFJIUw+1j9qBZRGY5/nYjHUmAITc5sN2j73fFql/twbWv8oEX03nB1Q1Sawn0RUqD3FlbnQxb+fhQKCnVGFVWkpSzGvuqolr4dZVySqP0vySysQWBWzfS2ldxnvallFOOUcoZiX7uknLyRehAk8zvkhNYiFPKcUkD/dpX5XKKVDckpRze7Sr7jandwNnPoPfbL48S8OTxeldznurw3CHg0W8DyFGQpDxmW6TGtYGNe007iMPkbn87Y3B5VGKUFtzjV2aoH+Hr774aeMPdZPvF4DwPBcLHtkR38UtbjNWiMkF9+f5P0HWu/hmRthEzL5QqNyun7LpSnQJedxtw6Q/5rxFUyhHtuVBUFlQJdoOalKOCo2xf1W1TWvlatVmlPhZTpruuokB5CJwGn6VDErgO8xw9BBnAG4QIUlwDUo6luNTHGCCxRZBy4ogQpwsb9pixm8vqzvcCX/kT2jG/2r7wsYg1J+Wsg1IOj/0zDwp7MnXdJAIEl7kkPkzuogDxsTtJUcYlx45uAibPUuePIWyWx4CffA/1/2y9JvuVoFKOSDP/7sR+ZRWVV0o5gsjI8yUOurMdTFyZsu3Ot98NzB8ytkFaKUeM0T61Qh43ed606Vzqn2ceVKQckS9bL1aKGI8AD/8XkclD4L5N9pGazCzK4eynAz/+LpNuiSf9MCmfxM0BxrbR2Cefae/9FFkYPe83ae4yDKUcwOTZ8X1ERkozx5bkshBBtFgB/utvgT/eDjzwBXMtvt62S4mwAtCGguN3++0ueZ7D1lWAIdoe30fErY3nUH4UipQetq+aURsVpg8Ygg5ARB6fZSVA7WvxqDm+1wEOfQ1AjkgWG881zy/drrLgdeaqTHJmNSneKGEp5XBdFfPu0Lw4lyMy96u/GFUSAmiec93v0HvLvupida0YEvyTfpjI6l/7G/vzuUNUDyUZecuFSpmop0g5PvuqRduKijGykdIWIuWsTCtbtjKll5+dRj3ziDj7qk6L5uKFCtUHzmdWH5w9aNtXFauKlKPIMbMP0zx5dCPZZi48Arznp4G3XgL80w3GZk1i/8foOfPDv0Rkfd6YwWSlbpvycsMej1KOInzd8IfAtsuAL/whbUwtlEn9ltO674P0Ovew326sX7AKz9ZLgYe/lm4DXBL4XlyCF2Nlxij7yufkEB653dgKAsDdH6U5kSQrZciAjJSTIcMZhU7XH5zM7KsyZMiQoU/wYlW341HKGWKf2uvRA5T2nlbBMt4l5MpL5xyFiE6D/g8F5DUpZ1ntqKgYj3OJvu2rpCLGOpNy1tK+qtc1QalBSTmrqSPraV/Fi1mDECTWEq2V6EKbDhqtUq3Ksq9a4/s+fiftEJoe4AFa28ukIOVwn9GPcku/YPJDJ0TKEf2l/r6rrKYcUk7B0/+kQa9jFkOTLOKSSDn8vdu/RuyrpFJOg87FQeB+FoM1CUgtlofSbtlXedSAuKzdnX+FErWJbteco1Ayi+jtutklK0k5csedhBtc1+lW6dH2VbMmsDu5039P+bxtmThIAHvX1fa9ugE6JuUUq8DUHqonllJOmuCqVMph+6qQUk4K+yofNpyj0qt2C3bbNvFAq9ssRJVy0uzgdVGZMHVY7jxejX0VB8hmHrIX9HM5o/LBWEog5ayVUk5IAci3S1rbV6m+IB9SymHSitiVGkkvk3KchfSIUk4CKYevt1p1CE3KUYFAl8z3zNcCF34flQ8Hl2R9HNlIaR8VdkEb99LC+OFbKYhUnVKWS0t9KlMlYOqs+LY0iFIOB5M2CGWcqd3R44DwtUcDSjlpLPLSgO/nvs9Qu2TLCE4Tzwu53wWonfnqYwjlUSq3+cPAF/8YOKp27nqVcoQViw/8m5YgcmqlnJohlNTnTABvUGjljAHsq3Q7jyEFyeOA9BaLEhv2UN4OYrEVwugWk+5+iJkSbF91JlpXudjzTOCVHwX+x4PAJS8G0HtiKuXw+DN0+ypBbl9r7H0m9Y0n7xHXFUo5cfCRclgp59gdtlWVxI4rqA9Kyrd83lbxk6oUcUo5DCZHLJ8gEunYNuqX24LI6NpX8ZwrbryY3E1j93f/H80VLruRPr/4B4Hvfb2dBs7L8rjJX56jSKUcgObI5QmbWDS+g/L3vk+TquF5zw+nS9tXiXw962lEsmGCL0B91JN+2J+Hk7uAZ70+vh8b20pjBSuDAsDn3kxkpWf8ip2WvpRyxLMHp+2cZ9Hr9APpiZRMamQFJx9e+L+BZ72BznnbO+mz8jiVxdTZwHnX0VwqVwC+/jaqQzs89Znr8JQg5ey8is5732eIYMMkLoDm1K5STrsGHLyZxpCtl5KdVVAp5yyqo0e+YT576KvKUrRE8zKeT8pnTUupb6/9fscV6l6kUs6Y+S2/xs73ysBZTwl/z5DzcyZ2x5FyKhPA024C7vmYUVsFiKQiiVCAIvmodcjRTbZSTnkcQI6ejWsepZxcjubZ/EzkYvagKWOZXq9SjlC4lUTnolJnai4bghOvV5z3XErf7MNmUwbbV0lSzsn99Pw0upna6pFvknXYRT9A39381mh6HvgSPXc3l4CP/XfHrk31NVO7zbODxPwRKqOJXcD1v0/50Foh+2eAiGW9HinosLJUSImmH7Da0zW/SP3e0e/EH58GnIchUs7CEfP+5L3J5zv4n/TK9ZJJdnd9aLD0AcA3/wn46p8P/vsMZyQyUk6GDGcQPne3n32bKeVkyJAhQ5+QdixaKWcN7KvaDQA9s1DDr7wY4VPKkQQcDoKEgty8ENRaVjKz5YBSjhgn+lbKWSe/W72IkMPa2Vf1hqOUM2gdkbs11pJkAZgd12eafVVrJUoUcu2rBiU9WfV8je/7wOfplQkM/UCnrZds1cXluJYkLq6XUl1IknKkshiDSTRy4b9QMvY3/YLtsIBk+yqLlOOzrwoo5bDFCcMi5TQVKUctMvWT3+71EpVySn77qpbY4SahSVFt2wKLyQwr0xQ0DyrlOHnkBtdd+6oG21fNCFKO3M3vQJKdWrX+g/a7xWJswbGPqk4Z+6pN59GCOY9xXJZpgqscJBjbKuyrPOksVgP2VSmUcqbUDm6907RtL+rzAmpjSVnijJrP0pIPJJgsAZiF+tKYWTQdxL6Kdz8vPhqdn5TH7R2AvCvUJd9wXieSctRx/SjlyHtxyVM6IONTylHH5gvwKuVoUo7HQoIhgzXW56yUo8aCuCCBTvsQlHIqCaScS3+I7IJyObP4X3BIYj/3abNADlCQZfYgcOQ24Oyn0WflcWprXK+GEUR+7m/S7ugQuE0z8SgJxSr0fDFul74+b8i+aquHlDNkpRyAAg87n+xYrUmlnGNmPFk67rdTi8PETuD2fwW++n+AO95Dn1lKOSXq77nfDt2fVspR4yEr5XQa9MfjQhqlnCSUxyjAJ+3W0oLbedL1V6uUc8XLgDfcNVx1k3zeWFgNaj3FSjlnonWVi3yegohjm6kP2H45KUs80bDmSjnrUBcq48BF30/vXfuqkL0Ug9ufpZSzk+ZPK9N+EgMAXPFjwKU/3D+BTQbRQ6QZi5Rzvnk/vp3a6OIxQxRhpZzWigmEM+LKNJdTJLoe2UzymLD1YuCGP3DI0B6CU4iUw8fJY8e2EJno5H4AOeDc54TTpUk5so8sE8mm3/EnDjwv5Dnr7EEikjz1JnMdueEgDbod22aSX897njqg5ydA+MBz/Tilx6t+ikgGT73JkFfKY0TmeMM+IpgUSkRUOHYHkR6kKh6D24AkiBSKwAU3ECln+gGjCANQeS6doLWs6QeN+s6BL1A92HYJEQL0HjePfRVAlj7j2wHkqK3x+DO5m+q4nBvIZ8lcQZ1D1dGNe007tZRyWH2xZF7TkNWTwOkY2UD92+TO5GenZ/wS/U6q5cwfNs9rjC0in6VSTlnZ8FUmwvZVANVrtgx1MX0/sEUpJ8k5rU8VkPOxo0g5Ur2z01T1XOUv5/PWi6ns5h4W/dOImVPyc/yjipwysomIMs0lWvN46dvJSvXBL9lrvvUFIuhf8aPANa8hch+Tkspjpr+cOkvZ6LmknEeovuTzwIU3AOc8m4iCV79S5csBah/TB4Bnvo7OIUk5D3zJkFf6wYm76f5YTfLBL/d/DgC48/3ALX+l7kWRbo5+h9bMPvdm4J6Pm2PZIoyvn4SDylZr5kFae+N1g7s+OFhaW3VSIvrKn5o6muFxgcfArD5Dhsc/Xno17fJ6zb/fbn3+ymuJqXx0voaj8zUcnlljK4wMGTJkeLzAp5TDihDDtEzSxAi29lCvLHXs7sJxg9FpSTnNZTo3BxRdQkK/ZIXToZQj5XY1CWDIhKBuJzlwHoJUykkiUoRgKeWsFynnDLOvaq7Qvct2ptWqVkl64nJxFVHWAgeUZHVjEFKOKPuk8mF53/VQypELoJqUUxD2VZKs17GtIvJFY8sykFKOsK9ybfwi6W2bRVufBZgkWkoUq5Q2XnSSBIOIfVU/SjnqfLxgGKq/lvpQKVr2XNYlJ2CslWxa5jf5ollUXHhE2VeNm4VCVjRALmrlJnf6ynRz3tQX6JqtFVJMAWiBK4Ri2RBA2o3+VSV2S6UcsQBcnqBd9bMHaRcZ71wuqt3JetG4D3LD1FlGWc5HirjsRuCyH4l+znUpLthSqlIwnHcLRuyrWClnUZBy2L7KIVSkQVko5SydoHyb2G76fm1f1UegfNN5ZnHaDXpVJmjXIYN3hbrBi7T2VYMo5ciydslTuj7LHbuOfVWh5A/muYQeX93QSjkuKUd93pdSTqW/eusD91XzAVKOxKiHlAMQ8UZaRG3cS/fRmAfOvkZdR/VHvKMziWyVBpvPV7t7A9BKOSkVpGS7lDu5I+dNUMoZ20L3zm2n26G+bViBcy6zpWOkQiBRGjF988KjwGZFtJAWBmkxsUONoTkTRIko5bTNPDREUOG6LUk5+ZIZ4yYkKaeVrFQTh3wBeM0tYVuuOLg780OQ5TgIKYeDc8PGakk5Y1up/a+HZdEwMbKByvyZrzvdKVl/cFkPXSlHjXXrpZp0+Y+q6zmknKS+m8dvOZ7IeWYcKedl7+w/naN9KuVMCYvF8e1KxUfYVxUVKYf7QraQAZLHCyZaXPWK+ON4riLzMu+QcqbOhiZJVMbNsYUy3Q+rBe26Kt4akH+3Fv2bBKuMMSnnIRWcloQhtvhNu05y4m56PtqqyH1cvtsvN0qOqZVyVLuRKkshPO0mUx4+AvH3vh54we8BP/FuPxHYp5QDABe/kMZteU+Asf5aPkXzFCbE1efM3H3ukCCJeeyrAHqe2n6ZIQNxHk2dBaBHJA69AaRi5sXVScofbicb9hKZ48LvtxV9uA5pZZvicEg52rpNXf/GtwPP/a3434xvoz5j3wfpua/XI4KFm+dbLzLvRzfbSjn8GrKvAqj9+5RyOi16hmU1O64nuYK/j+R+sd2kP/5f2ldxXeJzbTqf5u6WfZVSykEPen312B0m7VwXnvpzVK7nX0d16/idJi0Hb6Z54vkvMOV7Yr+5NpfD5FlUF2oz9ka1+SNmnSKXA17+LuDVnzN5MX2AyiVXIKLl+deRclOnReX0odcA7/4xv62WRKtmE1GO3012mWNbgN3/P3vvHSfXVd7/f2bulJ3t2l31ZlXL3bjJveFGCWCKaflCAFPj0CGEtF8gCYQQShICIUAKJTSTUAx2wGBjg41tXHCRbdmSJVmyLO2q7e7sTv/98ZznnnPPnDv1zu5Ket6v177u7JRbzz1z5zyf+/mcAdz/jdbGbW//DHD7p3WbiSfo9/VD1wO/+mwwvp5vjkCMRJDTh0hU5KKYB3bcRb9zpvbRcamUyTV4zyN6H9vs2xJeo3jsBvrtVsqT287O3wJfuQqYHGt+u4U5hYhyBGEOcPYqd57p9+6lzv/Pv/8wzvnYz3HBJ0I6/ojJFUv47M82Y7ogsVmCIBym+E45ZX2hHvPoR0OrggsXtjCCi9X8o67KKccLFqN9UU5I4d4X5WSpcOCFOOUE4qsadMrhwYIZi68y3VI4vipqp5wo4qu82oKBWsyoKEfNf87FV02B3KOMNsnHol23Kj4uyUxnHYKmD9LdQ0BropyCQ/wS+t7J6s9Ejd0/AfXjqyqloGDHM4rZrQjB+PwCqmP8bMpFPehdK77KLlCxSwL3gU6nHBblhLSfb78O+PEHrHVX+8XvW0L6hyqnHOt9fKyrnHIMUZQZSdS3mPbZgR1KlGM55YSJpGynHDO+Kj8BoKIHa/mOr7D4KkBZ+HNc3lTzsSAj62mALxanfchtaeHxwNAqGmjc/xQV8gHDKWea9g3v95qo75SBZSq+KucudG98K3DOHzo+3mABbXBleHxVl+FslM/ScU7xgG8r8VW9dNwBGqTvma+cciwxcLMFffvO98Dy7PiqWHWUC19fNRpf1ZRTjiHEsbfLji4wn/PdA5RQDQhxyuEBcJdTjtou+45rfi87TNnnr4uz3wGcfE3999XCj6/aRdNGRDn1RBOmoMUX5ajl8IA+i+M6Cfch6WacctS0VrvjtlFLlAMAWeWWw/1a1E45QLUoJ5HRLl5T+4LuIc06FWx8O/C8T1DfmlWD8gGnHBVBZUcZ2vjxVep6kp1ymMwgFdumDigRYoSOCs1gO2KFkeiCf/5H7VDSDn3q+7VVUQ67bxwO8VU2rUZ2He5w+2tFkFuLwHfdDLDucn39BjQRXxXilMMsPCG6dQTUfq4TGWZeh2WG9PdmHzvl7AoKGc1rDb4+Ber3LSe+HDj5lcDK82q/rxGnnGSXLrCbTjk983WsDmC4xoSw/krglV8Liis6AYu1fVHOL+k50xEGoO9DX6T6DHDXv4WPCW2/k6YrNurPAtQn9qsIy0ZFOb5TTgOinL5FdB2XGXL/Djn99cAF7w0XS/KxmmcJiddeps9fc79wfBVH3aw4V7fZoVVKWFIh5yGgum813WFMAT5//3Dc58Gdxu+HpL7W5mXxvhxcQZ957beD4nrfKccYH4jEKYdFOWpZK88JimnCOP4lNFaz5Rb6rVScrhblZObptukU5fTT2M/UPjov01a/3buAXA3HdwP3fZ1ihMaepN+u5aIWWfO+ycxzf/fx7/bidDBm2VM3vwTiq9S6Da/Rvz39+Kru4D4fWKHPp8wQXd8PraFrRQBYo6LtTKeaJ26mvn35Ru1AyWMCpihnYJluw2aElSnK4W0eXEH7oH8pMLqZnGHWXELOeWsupTGIHb+h5UwoZ7Jvvhb4n7cD//Zc4HtvBbb9Ws9z94PA5zYC/3wGuUSVCsDoY9pF6rx30vlix0Jl9wHffVO4AGbqAPDsQyR4m9xL28LCwZs+TNNd92p3JI7qWnQirfvNfwV89SV0/G123UtjJXwD0Oaf0vSC9wKIkein6jP3A/94mna+tLnva9Sm5x9Hj79/HbD9DmDz/7nfLxw2iChHEOYAL36O++7UpYOZwHSm+MebN+PTP3scn71584wuVxAEITLMIrPvqhBvTxDgggdvQuOrrB9kdjG6VM8pR83HvPM/kfISVVkAACAASURBVK52Rig3K8op6R/hMybK8f12jYHpqEU5pWjiq+xieqlAP5YevaH2PHhfpnqbc+JoBT/CZI455fCAgbn9dnxVq5FpfFyS3dXnQJRsuZWOZf+yFuOrTFHOXHLKMYQopiOLLwoxHbRKSsjI7jZGdnwrQrBKWdvtx73abaBU1INNzvgqQ1BkkqglyinQ53iQK+z83HEX3YUVWB6Lyur0LaagxhXR5TvlWIOXPBBZLuljxVFhfYtp0Kc4RYOGpigHUCKpMKccFiIY8VUsMmNL/GcfIqFHLdGI+Z1TmG4+lifuAUueo79z/LtcT6D1KGRpGzhOwHTKafQuc/6u7V9G29/senI7rzewPLgiGF9lFrC7+mkwdP9WujYw46vqRT244Ds3ARo07BmhwVrboa/ZQvn6q2jqiq/KW/FV3cPVAhb+XKOinJadcuz4Ko6wccVXqeN37PMoMsJcPuCIr2rGKUedP75TTgPxVedep8VPrZJQrlJ+fFWNNsTrXM9JhAfWu4e1+MYU5XQN1r7bPiq4qNZofBUXQQeW1y7y+045IedxN9/Fr0Q5fgE2KlGOsT1VTjldtDwe5DdFOc2ew8deRQLDzKB25qxyyjFEOWFCSj++ioWcqeD3aqKL2sTUAVVAmiEhgE3Kce67iMX0sY/aoaQduKjeTvzUyPrDzynnaGbdFcBrrw+KOaLAF+XMUFtIZshhkPvORuOreubT59Zepp9jp5yh1a1dE9Ui7ulrjVCnHPV6zAuK3NkpJzumr5GTmeB1wtAq/bieKGfdZRQZUy9irBFRDqCv11O9tJ2pXi0wZXHJ6otrL8tLkktZp0VyvD4Te2ncZ+svqdhtLzfZpa9lH/gG8OP3A5t+6J7njrvoGLGouGuArrV7F2hRQLNOOfWcHpnnfxK49meNvddm3eXAy75MDhkmXQPAynMBxLSrCEB9RX4CuO+r+v9hFYtkimz2PkZT+/uke0T/Th1aowU/fUq41W+4r5q/H/h84XOSzyNbTMTYznUb3wo8p44rVCOY8VXNsPoi+g37yA8ougqojq8C9P7oHqoW5QwuJ2eXqf3UT9jnbu9CEnN/8RLg++8Afv5RihIaVfWyEUuUU+s6ml19+WZLQN/wFIivYqec1XQsxp/RN32muoPXOCvP1Y+7h0jQ9M57tRCybxE5Sz1xM8U2ff0VwAPfpMipRMoQ5WzS28HjAhxfBQA/+RDwrd+nMchDO937GaB2+9iN9Hv5hJfSc6svpm373be1G/XLvkTvefwntD2P3wh85w9oPGTX/cCXLqd9FfOAr14N/Pyv6X8WdW74PTqHbvuUHmcuFegGq4e+C9z/Nff67bgL/hj00/fQTRerLqRzMzsKLDuLXnv8Rpoe2kn7cuGJwDMPAPf/t3r9/2i59/6XvrZ/6naacnQwC2dWnEsR0vd9tXrs9v5v0PqYopz8JHD9tcCP3kOuPKe8Gjj11ST62fMItZ1Wo7uEOYOIcgRhDpBOeLhg3UjV8x96/gYsHcxg42r9pV4qR1zAdPDFX24BAHz+lic7vixBEISOEIsBiJFIw4/OUQXmKEU5vjCCRTnqInvqgPuORlvw4TvlhBS5uQhUsEU5tlNOs/FVRf0jfDaccvhutqjjqyrlYIG7qc9WDKcca71y43QnBg+EhFHKA4jRj1kebOoUviBsLjrlINgOy5Yop934qk475Tx1Ow2GrL20xfgq4/ws1Tm/uJ101ClHrYN5TFjw1zVgOIuZTjllyymHRTktxldxHBZQ7RhW9d46TjlmJKGJLcrJTQSjm0p14qtKBRpUsY+5H1/FfUu9+Cp2yrGOve+UY8dXeXr5ptsOQANee3mQzIqv4veFinKMrHqA3sffKb4o52EqGNYsdJtOOdOtFbDXXa6LU7xtC47X6wEY8VXKKacw1URhVYk9efB5+kBzohwegK3nAjRvJXDoaX2s7MLY8Fq6e7IwScVhbm+tOOWYIpnJvcopxxDlmIX0Zli+UZ331lBQZl7QInxij1t44yXJAchl12/SklNOjfgq3ynHOEa+KEf1Baf/AXDxH/MK6Nc8Rz9mEw8T5ajPsECzEaecqEj16niiWo4LmSadcpZv1Oc8H8dnfhd9ATkM3ymnwSgNbhdcOAh9Xx1RDhcM+S5+vl6JLL5KbU/voupiRVI55Yw/Q/8HRDktRp0FzlmjD/dUBBULQcP6bG4DLAr20kFhVzJDy5g+QCKf2XbKaUQU5OonZpuFJ9B+bCZq0OacPwSe93fRrZPQWRIpEmdEDX9PzaRr0vM/CfzBj9RyHZFLLrwE8Ir/ABYbMVVcIF50UuSrCEB/D4ad+/wdyi4W/P7ehTpijp0QE13BayvTQS4qEadLlGPHVwH6OtmMoOLvsmVnklhj+dnRrFO7dA/Rdk3uJcHCxG5g1QXV70tk9O9e3ue3fEz/vspnSaRTLgM77gxes1zwPuB136f/mxXl+E6PDTjlAHS91+p1USJN0Uqu31cXvI/+zOvJU15D4rD7vkbXrYMrtWhnaDX9vvDS2snXnm88rs+xodVahMLRa75TztP6d2Mirb/X08b5kR4I36fszsTn05nXAhteUHtfNAKvR7PuYok03Wzw2A1a0ORyfOTv4WRGi2b43Ft2JsUS7X/KLajh30KlHPDG/wM2vJCi2di1iMVT/JvPdtw08VL6hgvuY/iGJzO+angNiUBSPaoPqJCABKh2yll5jn5s/4Zh1lxCkVXXv4kESBuer38v9S2mNrfHGG8wnXKG19Jvud0P0nn5u2/TWEwtUU7uIG0rt410H7nHPPQ94NEfkevLSS8H3v848IEngTfcALzw03RDyLZfAXf+C33+LbcC/+979Nv3V58hFx4WIcXjwPnvBfY8DDyphD4//Qvazp754RFT2w03HnYPGlxBNxABwPM/Qf8/9hP6/+BOErXN30DizcIk7Z/NN9GY4Q/+CLjxQzTW9dD3gIUnUbQWYsCu++i93UN0row/Azz2Y738Yp4ERPEE3RTINw384m+BB79D4ikvCZz6GuCka+h9x72IRJZbbonedV6YUUSUIwhzhNs2j1Y9t2FRH3YemPJjrAAgm+988bRQoo59uGeWBl4EQRCiIBa3nHI4OifK+CoWRnA8jCGycd0RGU8AqOhBBy50hsZXqSJQflLHcSS6qoU3ZvG32fiqqIUxoagfDbGYEV8V8SLKJV2ca8kpJ0Y/8KqcctTgRT0BE9v7JzOdFVkA1YXZuUClYqxXDaeclkU5RVVgTXd2u6cP0KBGZkgPnDSDGV3WqFNOJ0VcZat/AnTkRc+IEiRY0X7slMNiBTv3vFkqZT0YWje+qqCLts74KkNoaeJyyuGBsWKO5lvLKefQLgCV6mNui4BCnXJYUJNwi3LCnHLMeCnfwcgU5ahBPzu+CnCfC7ZTjj//gv5O4eL85N7a0VVA0I2n2IJTDgCc+07gberusfnHAWe9lRxNTFFOIL6qBaecrkE9IDq1v/lCdyzeWHxVpUyD2uVCtQhieA3dwVYp0/dAqpeOdz1XGRdmnNTkKN3Zm+xuP77KSwAv+BQdA5OeER3rAwCTIaKcjW8FXvHv9ZfD52dTTjnGb0+7nfnFdpdTjqNYz9cZZgGN5+kSMvlOOdZgOhdBfaecGRblANQ2a7nKNOqUk5lHxQszWosLANMHZia6ClCuCzHtIFIPPoZhd27776snyuH4KvX9F7Uoh79flp9ZXTBLZEj8wqKcodWGe1iLYy5dg1rsaS6Pz4f8OLXrMDEL/06ZMkS6Zp+W7KY2M7VfiRDbEJW0g8slq95755Io59TfB979YHuinIUnUFFGOLrxnXJmsJST7NLf4/x92IrQuHcRXcesuii6dTPxRethohwrnsePr1qkv4v2b6VpMhOcz5AhzIjq+yLm2JfDa2kfcZEfMEQ56hpgzXO1M87xLwKuu6v5WNlOEffoemRyL7D1VnqOY2FMkl1aDHpgO/Xtex6huBsAePDb5Mhx+z/Q6xy3CZD4nsVeTTvlqPOmUaecTrH6YuC5fx58LtUNXPwn9HjeMfS9PaLawbxVtM/WPle7dbrGGdkNZ2i1dhPhuOJ0H4ltAk45ScMpR4kwVpwDrL8ifN0XngBc+/P68WzNwtcpzQj5meNfRNcp93yF4mNN0TNz0R8Dr1fiwkUnAxe8n/YnACw7g6ZP3e5uSyvOpjb4+h9SjNqaSynu7vGbSPzB/WNDTjlp7ariX9uo39lmfNWFHwTerEQl3B88+iNym/FS+pzPzNNxTvy/ixNeSu3+8o8A191DLjUsQvESJHbxY3p7dH85sIzazgeeBD6wmX6L3P0leq0/RJTDzkFrLwv+Bjz1tXRduuM3et93D+nvlfVX0vbd8xXgke8DJ7+CzvdFJwHv3QT8yU7gvY8EBfonvpT60If+h36n3f1lcqk55zpyAx7fDUyOkbCG2XYHiWZSvcATyglrYDlw+huAs95C++XY55PoJZ+lm3EGlur9vHwjiei23gbc/il67uH/AX72lyQQOv/dKnpwKYAKfX/EYuSgN7BC7z+Alp8do3O/UgI2/QB4+rckSjrjjcAfbwPe8wi5tfUvBt78c+DqLwCrLyHR496QiC7hsGCW/EcFQbD5qxedgL/8wcOB51Je9YXWZK6Evq6ZGRBZNDBHLu4FQRBaIa4cTwJOOVHHV9lOOYZjgevHcsCRIq4jQZwRLWVybIjFqRCazyq70nS1S0ggvqpRp5yUfjwTsJKfhRn0ZMTzr7QuyjHjemzhlu9oVEeMUCqoAY5MZ+OIACPCZA7FV5liFLMdVolyWhTGVdQxSqQ6K8phV6p0H52bhenmBjxdMVFhuOK+oobXwVyvSVWUZEt6O26pUlKRf6rP8p0mUq25FFVMp5xEbSercqPxVbYoRw0ssiAuP0mDwxO7adsrZcMpx7ENHBNjRvgAhrCT+5aQdS8bghrXNvI5W+WUw/FhhepIooHlen+n+3Tx3Y/jSVa3Md9BxXDTAdzxVYCOFAjDFP4UploTmJgF40SK7kIDdBE72U136gHaKacpAVCMBvZ43xanmy90b3hB0ALcBQ8EHtheHV8F0MAbF/xTPXQOXXuzHhRvhpSKr6pUSCDTM0JtpN34KoDuTrTpHlHF9yKd7xPP0uC8zdDqxsQbgyuovTZT/Ag45djxVaoQZoqQ+DmXKIedQ1zuO659xm3UvsuU12P6ILXNmYyQ8ePPBmq7WXEBoF7RPxYDXvOt4HNm5JJZeOwkg8uB6+4OFh5rwd+/DTvlhBRNWZTDTjnFiEU5mUE6b1dfXP2a75SjijF9i+n9E8+24ZRjFDvM3xzcDsZ31xbF8WeyY3QOpXqCAp5khj6/bwt9d9Zzx+oUvNxGREHJjIqQnEP3n8bjjbtCCUIt+LtuJp1yTBqNr3LhJYD3PNyeOK0W/D0Y9nstbYlyuG/sXaivq7bdQdNE2oqvMq55IhPlOJxyFh5PhW8TX5Sj3veSz0Wz/E7Rs0CJcn5Jv2Hmrap+T/cwRVwBdD197POAPY8Cd/0bXZ8+q+oiP/9rmq4IcQLqV+4vTTvlzLIoJ4xTXwvc9UVy5OD/4wl908IJV2uXDacoZwmAGAmYE2ngdT8Ajjlfvz6wNOiU46UMhxp1flz0wfrruez0pjetLq3GVwEk/hhaTaKHK//Wfb3cPRS8VjZFUUtPBxCj344ul5uFJwBv+j/9PwvNtv+aYomYdANOOYm0jh1igWKqR0dvcXxVPA7EU3r9XvtdGp+Yd0wwqrN/mb7RJt0f3r8uPa26bzEZXAEc2EbtKpkB1l1JInL+DeclACTo+vZRJW4Kc8phl6YTXxZ8fuW5dJz2bSHnHptUD8WzPnQ9/X/a6/RrYf1uIk39x6M/onZZypG4xkuRSOaxnwB3/DO52K69DDj77cDO39K0UqY4KN6W5WeRmw9A8/zNFyh+6tAz1NcseQ79Hjv/PbTc33yenHbOvJbcg379T8DiU3Vk19AqEvTw+Rv3gDPeANz8V+RKtOA4iu/rmQ+c9y5yxbnt0zRW07sIuOyvaCzDdPZafApNV19M0yd/4RahCYcFc+iXiiAc3cTj+sLh+refi0+87GQM91YP0Pz0kd0ztk6LByL6wSEIgjAbsLjCLKiyUCcqWIRQLlLhzCyOugo3/Byvk++U4yg85ydoXdl2lu/8dznllAr6x3m94n6lQvuFB5tmOr4KKlos8FyE8+cfoy055cSrI8YAwymnnignT8vn4ksnmYvxVQFRTohTTjvnYLlEn/ccwrQoKeVpGXzXWLMRVqYgq9b5Vczr1zvZXngZZr/BjhhcpLTFaL5TjiHG4WkrQjA+dkDt+KpyWbmMqMEm1zlni2QYHrAx46t4AI7FT4k0fc61v/kuqlCnnDrCTo4J85LVIieAREJeuvq7wRfllPRnuDBqDnile8li+wX/AKy+SG+PfS4Up5UwiAVVhihn2iHKqedWkTBEOc241zRCMkPF6aHVhrOJWl5xujmnnMxQcNCu2fV85dfqW7CzU8eBbXS87TY47CjcLDy+tYJsuo/abe4Q7Qs7vqqUV9c1EQ3pcF8wtY+uEyb26IiEVjjuhXRnZTNxT6ZYJjS+ynie5+263uL2ZLaJWqKctZfRwKpd3OFjnDs0sy45gBYR1rPz536uFScT04FnppxyALqTtpbQyISdcuqKctR1bVib6xqk48n28Pw9EFWflswA77ofOP2N7tcK0+TI5qWpnfnODm045TBmgY7bwd5HawutTFEOR7rYTjn9SylepDDVuWJ6PZqNr5pLLjmCECUsKnAKUWdi+SwkaUGUA1Bf12i/3yz14quSXfSaL8ox4quG1lAxdY8Sg6R6goLeecfAj0mNKsbPJcpxsfhU6pdn8vu5HXpGSGz61G0kXnAd75H1wOhj9Jvv4A7av2ufC+z+Hf0W2rOJvntiHh2zRSdXzwMgsUQsHrz2rgVfK/L17lzDSwBv+inwkn+h//uXkACA9+H6qwyxmGO/Hv8iKvpz2119UfD6uJ9FOUZUMr+31XM6KnxxUAuinGQG+KN7KeKxlf6la0ALoRoReA2v1TeSsCsMYMRX1dgGL01jGyPrtZvRudfR796p/W7xcyxGMdAnXK3dbfi3zcAyEpmZfVsr8O/bVC8tb9npwIv+qfo35vqr9OMwUc6qi4FXfUOLU8ztOOst9NsyzGmJP7P4FC1AqcfxLyG3z5s/Qv3k0tMp+qtnPnDTn1Jc1+mvp/7lay+l/b/yXC0eiid0LBuz8nz6/J3/Qu8fWEbimD/eRoKdleeRgCqeINelc66jz13+Eb3PWIxjXoef9nr63C8/Sf3cozeQC6KXBE55JXBwO4kQ/9//1Ba/Di6n+bLASzgsEVGOIMwRfn+jHmQ6bcUgrjnTfUfln3+ffihUKhV85fatODgV/R3yrzyDlj1VmKFCrSAIQieIe/Rj346vKndAlAPQD9x6Tjm+KEf1r34kiEOUw1EJHC0ytV9nP9vCm3JJ31nhEvjY7wX0j/CZEuU446uidMpRx5ULCs3Om0U57TjlcJzJTMRX5ZXIoFyMtk23AwsfAMsph92q2o2vUvFkibT7nImKYo4GjnmAqtkIK/PY12ozgf01A045pnhjcpTaOg9+2c4ulTL1V76wQw2WJVL1+xgXlYoVXxUmylH9ke+U4xLl1HHK4baXH9cDYxz54ztZuZxynlafnw4u1xeVebXXnfdLPEHLsQVFhay7WMyFRvM7xHTKYdL9tM1nXmsMovZpoQ1TzAWFCIH4KhVT0rdYL6MRUQ63neJU9AXPY58XHORrxyknaQxmRlU4MelfSn3Y/m2qv7dFOYbzR7JNVwm+43JU3dnYtzgYX1XMte6w4YIdYrJj1OcVp6sHKZul2cFxs21ViXIMm3f7uZpOOcY8ffcoR9tYcioJ3ux1jhtOObUipDoBD87XE+VkGnTKqbUMQA8gzzX4XDDFhC7qxVfFYuQIxU45fnxVhGIrPxLSIpmh74BnH6b9zJF7QBtOOUYBxuWUM7q59jG1RTnmZwEqYq84m65V9m7qTJ/aCL4zXAPLT/WIKEc4cjHF7bNBO045nYbFqbWuGzPz9LXOiS+liJh0L12bv+UWill9zXfofTyfeELHxyYy0YmKGt2X89cDH96lC/hznZ75wK77adzKFV0FUDE8O0bxMqU8OX0sOpm+I8eeoGL1mkuBKz4KbHxbuHB1ZB3wwS3K6aQBhteSO6jLvWeuwK7YLrr6SUAOuMcZj38x8MJPh897YCm5wvJvYM9whJrtc5qvPepd74bR7nnJEVa1oqfMZR1zAT0OiHIaia9SbfnEl+l1XvIc4OX/Tse0keUDWqw+sIzmM7ii8c+6YLedeo6I61S0WXogvM3E43STi+taeOPbKIoqzPlm7WUkxjn/PY2tN0B9Rbqf+pyTrlEC8zhFPBUmKWrq9z4LvOsBEs2su4IcpFiU07+k+jvVS9Ax2vEb9R7lymXeQHT228nhpn8xcMH7gLf+Ut80BWghpXkd3jMMbHwLuQH97zvod+V576bXznsP8K7fkaPpgg0NbPcl5DoU5Xi6MKNIfJUgzBFisRg2feQq7MvmETMuKEZ60xidCBYOKpUK/vHmJ/Dpnz2Of//1Vtz2wUsjXZfFgzSQ8asnxiKdryAIwozC4oqoBAEu8kZRvZQPFnKdohwr/qSWUw6LcvhOjOkD9APA5ZRTLqhiuUOwY8MFbR60nilBRyDCqANOOab4Cmg+IinglGOtFxek6wmYSgUqGiS6dPG7UwQEYXkgPgeKEPWccnxhXDvxVXE6D/j86ATslMN3Lza7LHPba7krcXEd6LBTDsdXGf1MdlTleHOklCU24agw+65cLwVMtdC2A/FVXnj8mC/KUYM1TlFOiFMOD1CxU1F+Uhca2V0kru4KdPWTZt54blwPbpWt9hvWb7FIIxZTIifbKSfrFmr4TjlFhyjHdMpx3E2bGdIF5l33kfU7nyOMK76qa4A+O7Fbf8eEEYivajLKrRHsAeREmvaDLS6qRaob6FtkOeVEKFhhvCRZhR/YpuMKTaKMOGABCNv4z1tJhfFCVjnz5aN1ruBC1eSoFqK0K8ppFrPwYhfg+5cAAyuCNt214qtiDlEO769mxAWecX424/oTBSxGqXcHM99R3sqAvDkAP1fvxF97OfCSLwBLTqv9Pi4Q1NpfPSM6Ys4X5czA9VMiQ98R2+8ETn01Pcei0VadcsLiq/h8KE436JSzTwsKbaccjvQrO5zBZop4nNal0fiqmTiegjAbzHZ81fBaEk+EOZfMJr7zWI3z/+p/1cXVJc/RrhMAXTMsOon+AMO5Q8VHdg9T0Tcq+BqlESfFVr8jZoPeBfp3JwsXbEZUMfyJn9J0cKW+QeCJn9Fv1AXHA+e8o/7ymnEHWXku8L5Njb9/LnLqa4DNN7XmitK/TAnv1W9BL0XXgMmecNeTmYK/31uJr4qCZWcC9321dvSUyaoLgQe/DQw7RDm15sEiKNtF5tirgLfeVt8RkjGdcgDglFe3d43Gy60nyulbSCK4Vm7QAqjfq/XbNdlF4pZmSHbRDT6/+xZw8jX6+VNfA+zfClz5N+p9GRLRnPcu+p/7of6Qtn/SNRRhBZCgzcaMQPMS1c4+C09UU0tQec4f0XjNrnuBS//MiFVLaMeiRrj8o7RNnXKfEzqOiHIEYQ6RSXlYmgoO4P7svRfi1I/8NPDcvdsP4MaHKcZqx77oizjFEiktT10+SxdEgiAIURBXxX/bKacT8VWALiIyLlEOD6KVS7qoxp+18Z1y1CBFpaziq9LVBe2SKga7BDs2viinjlNOdh+tV9+i2vNrFF/Eb8RXoQNOOe3GVzmdcmocJxMu0iYzlMPcSQJtLzc3ihD5EOeXgCDLa/2ODi4KeS26tTRKMUfFVy7wmfFVHBVX6y7VgCinhpCrYIhyOuqUw/FVxjImR3URHgjGLZkiFNspp9X4qkrZEgCF7JcqUY7jOPtCyzpOObkJKs7GPN02vWR4P3nwaf3YFOVUCTvDnHIKej/ZzkMA3a3lKuqbTjZ8rPg5c5DU5dLRPUwW8ADw+E3A/V+nu0DNogR/95UKKoIkRf1FZh6JcvrriHIC8VXTWvzUKUynnEZjn675Ku2LQ8Yx7IQoB6CM+H1blHOXNbCY7iMhy8Sz7Qs4eNtZlDO4ggrTlZISAeej3Ua288+O6uuX3jbiq1rBdAyxhTNdA8B7Hgw+xyK3Wk455ncjz7+ZApc57xmPr1JtoN6dw30LgT/8TWuiGj8ia7C9u2w7SbJLC1lqMf9Y4NXf1HeSu+hfAuz8LX0HblUD/40WYdqBv9MKk9q233enafE8DouvMgsetWI9/M9UjHUx23uGfgMMrQH2PTl7TjkAnXuNiBDT/fq8EYQjDf4+mi2nnN4FwNtum51l18MX5dToT00Xg3rwdTR//3YPR/tbrdH4qsMNvpYcXusuZAPaoWLzz2g6uIKuXxJdwAPfpOcacYo4GjnuhcAHt7bmbMPHY/9TNPWSdH113V0zL8K3GVkPLDyp8ciiqFm+kaaNRpudcDWN9a2+WD/HfUWt6N+eEdrG+eurX1t0YmPLBtSNKD1aWHjBexv/rAtflNOAI+jVX+zsuFUrXPKnJOA3XWnWXEJ/YXA/FCZIW3qavv41nYsbZc2lwB/epZfD9AyTE9D9XwfObkB4GMZM3ygiRI6IcgRhjtPXVT348LLP/xqXHbcQm5455PhE++RLVIxZPdKm7bogCMJsUuWU49UuqLaCGT/TVHxVKVgUdhW5bVEOQIUkjhKpVLQyvlxUDhDlJpxy6ohyfvJB4MAO4E031Z5fowSEGWrfRBpfZblntCrKYTGXCTvl1BMjsHNBMtNZ5xOguu3NBQJOOUb7LjcoaqhHuUTnkEuYFiWlHODNc8dX/ddLKOaE77pxERBMOc6vB74FLD45uA2mQCdq+I5FxRTCUwAAIABJREFUs3/K7qMYD8YUo5l9plOU04Igqmw45biEb/77Go2vilXbIvtFzyna78UpGuz2UoZTToIGIIuO8/PQ0zRfVID8hH7e7LviNURl3A8DbuFRPusu6vtOOSXDKcew8U71URSXa+C+ewjIqjt32TFnfFcwRgmg9SoXKOqK2zUXMPpDBs2ZKlFOh8Qu9vKK07UHNk0WHk9T8y7mKKOdTEbWAQ9+h/an5xhOGVpDopx2BRx8vPc8QtvSs0DfwZifJLFopPFVqj+YHNVtfMadchwOT7XwnXIcBUqXU06t+KowTOFVvTtIo8aPr2rgRhnTQr8ZvCS1o7nqktMsxz6v9uuXfBj48pXAV64CxjYDZ765uTtTW8V0zmJRDh/XVvvUsPgqs8024pRjzst2ygGAY86bfVFOqkFRziV/CuQ66KQoCLOJ75TjGGM42hleS/unZ0E080sYTjkACZJyEY6/+6KcORgF1g68/8OiqwAqgid7dDTM4HK6nl54AolmAXLKEdy0GjXFv/f2baWpy5V1tuhbCLz99tlb/oINwKu+UbvdmqR7gYs+GHxu0cnAy76sI55cvOifohn/7B4C/uRpd0RUKzQjyhlZW/89M828lc1fy887hn77Lgzpa2Ix4Ky3AHd/qTVnqlisWpDDXPh++hOOauRKThDmOF48hmOGu7F8KINXn6XVmaevbOFLoUHyRSo+TBcjLFwLgiDMNBzHwkVdL9V5p5y68VUsyikGxTMlh8DAj68yRTld1W4QPD+vUacc1bfXE+Uc2K6LvJGgfoDG0Jn4Kj8iqVWnnEp9p5xm4qs6fQdJmABmNglzfuHBh5hHx77V486RSo2083Yo5mkw1o+vMgZhxzaTS0bNz5vnpkNUcsN7ybbW3F+FTjrl5KvXKztKd+owprOL7y4WN2KrjPgqV39VDz52/rIadMpx7b+wGA2zb2TRWqqX1rkhp5ydekDHFGI16raWG9dFdNc2FrLuor4Zj8Pby8XHWEwPlobFV+XHqc1Ojurnbft+djjKHTLu+h2iY1JP+GIKQYvT7ccy1YP7z0ILAqBAfFWH3MOG19L3c3Ha3Q7ZmaJdUY4fX/UQFSzicUN4llXiwSjjq5RbSHaMvv+B1u4MbAdT/NeIHTffJejcD+yUY7QJP76qiXY1m045HF9Vzymn7eX0Be8uPZJZ8hzg9z5L3+XLNwJX/u3MLJfb4fBaKj4BhjtNxPFVpliw1nF1iXLMc4nXmUVELhHiTJHqbey8HVlL0QqCcCTC4wiz5ZQzl1l9MfDeR+u7PzaK75Sj+tnn/iXwon+OZt7AkeuU06tEOWHRVQBd342so9+GPQv0dw3HomXmzb5zy5EICy923kPTTt9kcbix4QXtnY+xGHDSy2u7cfaMROdCGpUgB6Ao63hi5m8+mE3iHnDd3cA514W/Z+NbgT+6RyKihI4gTjmCcBhwywfIcu1Hv9uF/75rBwDg7258tGPLKyinnKm8iHIEQTiMYUeOUh7kqqBcHzoqyjEcJFwDZr6LSyn43kadcljwAVBRbvRxctwoFUiMEvPqi0FYcMLzCXOsyI5FKyxxOeVEGV/FhXMvEfy/USpl+sHFYi6TRp1yyipGLNndWecTIDj/VkQSnSAgyjHWyT/2sfbOwXLJiK/qtFNOWhdCzTsjpw8FXVRcmC4sdpupVGg/Te0n5xTXZ6KG16FcoGiqeFzFVxlOOWa7D7iL2U45SS1SawZ2ogKoSPj4jcBjN1KGukmVU45jWdwObDhWqThF0VUADS4lDFFOPKmcWKy+LZ8FpvZRkXb/U0FRjh1fFda3jO/WxVaXKCc/6RbA8LaUCkERKTOwDDiwzf2d0q0KqVP7qM9OZGj77YFWL0Hznj6k77AcWkV3cNUr7njsXKPOuU6JXZiEsbxmo7LMwcRmIoqawXQhsuOrzNfbdspRgoyp/dqenOOaClPRx1d5SSo+ZceorWSGWr8bt1WadbLhfezqD3ynHNN9h+ffhJjJFCLMtFV4aoZEOS/+HPUHRwunvpqurRef3Ll+wob7TRa4AFpU0+p5HIivMgoG3C/1LKhdYDI/4zvlGO3dX+dzaTqbTjlXfezIK14LQrP48VVSyqkiFos2cpOvF/j7N2p3iCNVlLP6YuCqvwM2vLD2++YfCzxzf9DdgqOL5h8nRfBOMO8YYMW5wPZf0/9RCvuFw5u4RxFivRE5jR0u1IvtlX5I6CDilCMIhxFRpnzUgkU5WRHlCIJwOBNTMSNcuIrFtMtBVB1q3o6vMoQCLqecWJhTjqPw7DvlGHd8JdJ68P533wb+9UJyd2AxSENOOXZ8VQ1RTpQRTP4+j8G/gz1Spxw1/3bjq1xOOXx8XK4d9vu8FMXjdNL5BAgKOuZKfFU+zCmnQVFDPcolEpQk0q0JQxqlqPoM2ymnVCDBg3neuzCPvd1mSgVqa1P7gxFkzbaXSqXxfswUh5RytB+n9gdz0812bzrD2PFVye7WBEQcPQZQvMTik4HrrwX2Ph58H7dlvmvSGV9VcgtJTKccFk7Z8VVhjmKHdtF0wQaaBkQ5LCqr45Qzvlv31/EEva9svLeQdRf1uYBaLurtNQsui08G5oUUzLuV21F2HzmbrbmEhD+2mIVjx6YPaqv8S/4MeOON7vma+CIZddw7LspRTjmtRGWZQphOxVeZzhMu54gNLwROeKm+I7VVTAtx31ZcbZ8fXxVxkbxnhAR7+5+iwfuZxnTKaYTMELDiHF3ICcCiHIdTTjPtKuCUM9PxVSzK6bA46tirwi3Wj1RWX9SaHX2r8HfaMefr51hUE4lTjlE44HZeL5KsllNOIqPnObgCOOFqOtdmi9UXiwOOIPjxVeKU03Hs+KqoGVhK1xR9ETn7zBUSaeDst9UXvI6sp6l5rbxYOeUsOK4z63a0E4sBl/yJ/n82hbbC3OO13wUu/8hsr4UgHDWIKEcQDiMmcm6rfxbRREWhREWe6YKIcgRBOIyJq+K/WbjiAeioRDktx1eVgqKCMFFOqjc4GJRI60LjrvsAVIDpA0CJ46scDhA2XKDn+bhiZEpFYOpAxBFBHGEUN+KrIlSbsqCg5fiqkhYhlK3P8rEqNRpflVHih2i/nwPM+fgqh1NOvfifenAE0ow45aSoOJXs1k45LNSoJ8oJCO4cEUZAe045xTzwyfXA775F/x/aBezZFP5+s18q5kjAgYrDKUetq+mU41vlq0JAqie43o1SqegiQqqbctvzE8CD3w6+z46vcolyKiGiHD/aZ0qLclK9dBx5neMqvsoWHB4kJ0rMVwPBYfFVcYdoj5nYre3WTVc0Jp91F/X5veWi/i4wB0ov/jDw5pvdy8yoO7ym9pGYoncB8OJ/AS58n7WMJM3fjK9KdjVWbOD4KhaOJWdAlFMu0nnWrADIfH+nbNkHV+rvGZdTzsg64BX/3r4DR9oQYnDRoiq+KuIB9e5hirY7sC149/JM0axTTiJFwjJT6MDwdUbS0SaauTvYPMYz7ZQzU/FVQudZcQ7wnN8H1l+pn2s3viqR1kJE8zcHt9l6kWQuUQ5/1o4pfMV/ACe+tLX1FAQhGnynHBHldBw/vqpD37+rLwH+eGt9p4YjlfnqJghTlLPwRIqwWnf57KzT0cCqC3W0mIhyBJOBpUdvfyQIs4CIcgThMOL5J7lV9FOWeOZANo8HdhxoeTn5Ytk5X0EQhMMKdn4omaIcNYgVlUNLwXIrKeVr38UWN6KVAoX7kPiqroFgHIeX1oNEex6haXGaCphcbG7aKcchNJk+AKBCQoGohDNmhFEn4qt4/uxc0Ox6+045cYdTjtqndZ1yCrT8pBGj0ykCba+DrjHNUAhzyjGjy9qJryoajlDTnbMQLBqxMOk+Q5Sjps2Icuw2w6+ZTjmpvuZcqSb3ApN7gGceoP9/+hfAd98Y/v6yLcoZpcfmwIsZX1U2RVSWU06qm+bXrFNRpRS8k39gmVvgw+tQM76q6O5fPcMpx4yv8lJ6X3MEYJVTzk6atuqUU8yTu1jfIvrfjKRiCpPuor5niHJ80WQy+LpdIGX4GE7uJWFOz3xg/RXAmkurl2HHVzVKIk3bzEKnZiOlmoXPvcJk88KaeFy3nU6JcuKedqDoZIRE2nTKUQIZP74qS8cz6m3sHgEm9gAHdsyOUw5f30QSKcROOYYox4+vatUp5wiNrxI6T/cQxYSZx7Jfje2049jDbjumwIa/U5pyymHXHhblzHBbFwShPktPJ9eo/qWzvSZHPnx91anv31isc9ephwMLj6fpkCEeTaSBt90GHPu82Vmno4Xn/z1wznXabVUQBEGYcUSUIwiHEQOZJL7x5o1Vz++b0AWLQqmMUz/yU7z4c7/CrgP1CzxfuPVJfO4XTwSee+QZKjpNFzp4h78gCEKn4SKzeTe579ASkejQFkaUCvoHriuD1nfqKQWdPlxCmpwS5SQz0MUlI75q9HH92XKBBtIbcsop6XmZ/5tk96n1LLcfjVSpADd/hOIwALUPOuCUU27XKccU5YQ55TQaX8VuBh2MsCpk4e/HOSPKCXHv4WMTtn8bhWOLuGDbqdgus89I9+v4Kp6yOCGMgINWwf3a1AEtSOkZbq6tTO6h6fhumu7fFhSR2JSs+KrsmFqu6ZSTcDjlxKvjq7hIXG8f2FTK1Xf2JrqqhWvlRuKrim4xRDxOxfai4ZST7qV1z5vxVY5+8tAzNB1eR1Nz+wLt13P3mRPP0tQW5Ziix3zWXeg0BTy+U06DTh7slDP2JO1j0/3IhOOrcoeAdJMFBn4/t7dOFxFMEUWYGKkWvI87FV8FAMNr1TKacFxplkSXFp9VxVdlqY+NPL5qGBjdTOfh4Cw45TQbX1WLmEuUk2x+/vG4vnZLzXB8FYs1ws5r4fBm0UnAm3/udnpqlIxLlKPad0tOOer7qJW+VxCEzjJ/PfC678+8a9vRSNcAcPlHgZNePttrcmQytBr4gx8DJ18z22ty9LHgOODKv3GPVQqCIAgzgohyBOEw49w11YNyF3/yFnztzm0AgL/4/kP+85MhcVcmH//Jo/j7mx4LPLd1lO4mzhXFKUcQhMMY3ymnoIv4vigmKqecKV00K+Xpj4snzvgq0ynHEC2ExVd1DdAPZi4EeSldEOWicnGaiu6mg0gtfKecruD/Jly0N5fTKod2Abf9g47aQaxD8VXs7uGIjWn08yxCsIvuvlNOne/VclE7cQCdd8rhu/fmSnxVfpK2P56s4ZQTgSiHi+2diLCqVGh/+ndI9mvBS8PxVTktDLDbEu+X3EHtvJMZaq6tTCqnGxaCjO+uLcwKOOXk9efNQq/pYBSIa1Lnk2fEVwH190HVOpSq+8Rkd7VDkOnkFYu73anKpXCHEnbB4WOV7lfxVWp940kqNtr9WnaUxCepbnIjcTnlcHHe1W/xsegNEeWwQNRV1GchYbmoRUiuWCQX7JQzupmmPSHF+3iS1jE/AfTOb2zeDL//wHaadrpYa4p+WhEAddopBwBGlCink045sZh2y/Hjq9S2dSy+akR/d86KU06T8VWNYLbXVuKrAEOoMMOF0GMuAF79TWD5WTO7XGHmWHp6e4UxFtOY369LTwcu+mNg3RW1P+sS5Xgh8VWCIAhHG+e9c3auhY4Wjjnv6HYLEgRBEI5aRJQjCIcxp6/UVsd/9r8kxvnvu3b4zxVKrRU7NyzqAyBOOYIgHOawuMK8mzwecXxVflILI0oFWlbPcHBZ9joBVHxtJL4qrWJG/CJjV/XgRWGaCtdxdoBoML6K94nTKSdCUQ4LD/ZtpWnH46tadcqpGPFKtpBC7dNGnHLiRtRMM5FEzVKY0ndId8oxplkKU7TttjiMRQwxj4QNrZ5/lRLNwxemdcAhqFwEUNHCn0B8lRJqlPK1l12cos8B1aISs00c2kX7KtXTnFPOhOGUU6kA48/UFuWY7aM4reOrAk45hhjNd8pxxVfVEeXksxSlNRp0YfSPnUmyK+g2BmhXn3hCu7sw+7bS9rI4y0Wyi/Yxuxql++lY8nLCHMWyY0C3uq5O9+pjzusOGPFVjj6TXWT6Fqr1575evZeXX8spxxTlNCpKSGZonqNKYB9mRe4lgKfvpscrz2ts3kzPApoeVL8xTOeRTmDOv5VlpWZAlDM8A6IcQLdfPgYBUY4hOI4Ks0+YjUJUpE456joj4JSj5tt0LJq6rphpd4J4nGIc5G5mIQxXfFUiDVzy4frOTk6nHBHlCIIgCIIgCIIgdAoR5QjCYcivP3Qp7vrwc/Huy9bVfN9UoYhbH9+LYz50Ax5/tkasgUVPmgaYD04VUCpHWDAVBEGYSdj5oVTQBXYegHYJUVrBFEaUC7SsnvnBZZmYLi5mUd/plHOIXDoAPbCeSFUXKYvTyqElqV0ibv8M8OUr3evcrFNOu8IS311ExcGY+yUqcRSgC+WtCq9Mp5xKOeiGwcenXkxUVXxVB0U5ecMppxOOMa1QmCSXEVv0YDvltHr++U45qrDarmDMBQuwEo74KlOoUajhFFOY1qIcWzBlrvPBp6nI7opxqsXkXppOPEtRc9z3hFEuGo5eOWBSnd+mgCPuBV1dgDrxVSHbv/0O4KHrgfu/Hnyezy+TZKZajFQ2RDnxpN6ugzuBfzoNeOJmFV8VIsphYWLuIP3fpZxyWAAYT9I+z00Ez/HsPr0/0n30ur9O5v4IEZWNq/irMKccjs9yFfVNsWa5ACAWvn0uMkOGU06IC46Xovmn+oAlpzU+bwDoVYKQ/eTK2XlRTrtOOar/jdpFxmQm4qsAOt8Gl1O7AzofX+XHb8aBgWXRzrsRonTKccZXtSj68Z1yZji+ShDq4YqvahT/MzEdU+hJfJUgCIIgCIIgCEKnEFGOIByGLBnMYEF/F85fW21Rv3xID6Bk8yW8/it3AQD+6ocPNzz/QkkXGyYaiMASBEGYk8TjOjKEC2ediK/yhRF2fJWjqBoz3BMCTjkOUUV+Qhf2/fiqdHWRspijwnU8qQvSW24BdtxJhWYbLjDzfFyuD1E65Uwfsp4wnHIija8yiu5Ai6KcmD5G5udZqOGK0jEpsThKfRd3QjTCFLL6zua5El8VcMox1sl3Gom1GV9lxLQBnREjsfDKc8VXGW25VnxTcVqLV2o65eykczvZ1ZxTDoty8hPAmBJj2G0gnwXGVaRSqaBjcIp5csrpGggKCkyHKFPgxscsbsVXhYmSdv6WpltvDT5fLlcLTZLd1U45pijHM0Q52TFqN5N7aP1C46syJHCaPqidxcwCvJcABpbTe3g/8vwDohxXfJVX3X5veD+w+ackkIrFtSjGFuXw/nIV9T0zvipP/zfjjNE9T4sea8VXAcDKc3XRtVF67PiqmXTKaaEwzPu4k+KhxadQNMzS0zu3DIBiqxaeqP+3nXK8iN2A+PplYFnnBUcuvCjjq9Q5lIxAlGPH9wnCXMEVX9Uo/JnMoBb++U45M+wKJQiCIAiCIAiCcBQgohxBOIyJWQP2X//NNqxf0Of/n83rQuudWxyF2RDM2KtJEeUIgnC4wjEjpbwWoLgEF+1QMOKrinkqwGcGdcyJjRlpYrquuFwucuPVopxEV3URjp1y4p6ODRpT0TG77queb7NOOVHFVzGxuC44t3McSkXg+muBvSq2hcVGfoG7STcW3ynH4abkO+XU+U7kgjoXAW3BQZQUpnRswVyKr0p1hzvlxD3tYNUKlbKKr2KnnA7EV7mccrgNTzchyvGdcorVrzEHdyqnnExzbcUUk+y6n6blQlDkdvungK9coV9jkVBxGpgcrY45iicMpxx2NlL91frnAcvPosf14quevket133A1AH9vMspx4w5u/vLdD6ziMkX5ahjzMeF+zuX6BHQwsTpQzr+zyzAx5PA8Bp6PPakft50ykn1apELrzug47z8mK8KcM9X6G98N4lXuHhvCm2AOk456jMl5XjUrGDAPJah8VVqfVZf1Ny8ASXgSgMHlSinFaFMM7TrlOPHV3XQKSfVA7z2O8D8Yzu3DAB4+ZeBF39O/+8lqQ3nJ0mUGHl8lWo/gyujnW+j8PZEsV2+U47RXhOtOuXMUnyVINSDrwPRQsQZnyMZHYnuf1eIU44gCIIgCIIgCELkiChHEA5zfvPh5/qP//R/HsLNj+7x///1E6P+42svWAUA+JsbHsFPH3m2aj5lI6bKdMoRUY4gCIctXPwv5jvslKMGxLmI66WoIF9TlFO0nHIscUExHyzsJ40iIxcp/SK7csrxkvR6fgI4uINee+b+6nXwRTmqKOUSr0ztN7YxalFODLp40IZTzsRu4MHvkEsFYAg/2CmnyXmzaMAXbjlEOfWccsqqoJ40IkY6ATtAzbX4qvykEcdkOuVY8VUud6ZGYPEZC9M64pSj5snLSPfTOVUuBd1TTNGGTWFKC0JqOeUUlYgpmWlO/Dahr/UCwjtTnDXxrH5fqaj7i1JeucJYjipxT4tx7Ci4V38DOO736HGyhiinUgF23gMMraFjvu1XxmslR3xVt94f2+8Anvy57p+8JJ1LvE18XIp5FWMW4vaSzNA8c0b8n+n44SWBodX0eN8W/Xx2jGKgACXEMo61GV9lOuWUCrRd2+8kUU7vQv0Z3ylHfZZFVy6nnLghJCwVwrctDF7vrsFwdxN+ftWFzc0boD67dwGJyIDWhDLNEHDKacHtxo+v6vB6zgTpPu1yxaTUeVPMdyC+SvUL846Jdr6Nwsc7SqecgMhLzb9Z0Y/EVwlzlSjiq0xRTlziqwRBEARBEARBEDqFiHIE4TBnYX8XrjphkfO1/7xjm/84HouhUqng327bijf/1z1V753Ma/FNsVRGXxcNyEh8lSAIhy1xTzvScHEuCocWhiOoWBjBRWovRcXguOMyyxR8sGjBS1WLcrjgz4X9QHyVKirN30BT3yknSa+Zzje7aolyGnXKmap+vRnM4jag43CA9uKr/FgbJUC1hQQtxVd5QTcjho9VPUcajhHjNmELkqKCC/xcSJlLTjnJbnIKMkUmtqgBaO3Yl0t0fLhYVEv09OB3ga2/bH4Z7L6TMOKrAGrHgfiqGssu5nQh3T42tvgm2aOEJM3EV40C80hsHRDe2e5bxWnaz+WC7kOKOTq/7ZijRJfud8zjZeM75ThESfufonmf9RZqB1tu0a9VXPFVXVqUk5+kfVo2zmMvqUVNAaecUvW8/O2o55SToEigmAfsU045hWlyPetmUU5f8Fj7gr84LbdiCW2m9gE77gL6FhvL4X6koLcPCHHKMd5bbsUpR613WHQVQN8d3cPAghOamzfTM19vd6eLte065cxEfNVskuym9lrqgCinZz7tvwXHRzvfRoknAMSiic7i6z2zvS49A7j4T4CV5zU3Lz++SpxyhDkG3xjQTOQhU0uU02lHNEEQBEEQBEEQhKMQEeUIwhHA6889pu57Pn/Lk7jpYe2Qc97Hfx5wxzHFN4VSBYPdNBg6mWvxbnZBEITZhp1yzMJVq4INF1xMtkU5iTSQHghxymH3hKIuMqf7qgv3XBCuiq8ynHIWsCgnR/PzksEC5sj6EKeckl5P83+T7BhtA8+/HaZtYUosGnEUr/cki3K4cJ5obd7lUgNOOQ3GV3GbmD7Y3Do0CgtC+A7pdo9RVBSydZxyvMbOwRs/DPz4g9XPV0o0D97uWvv3F38L3PmF5tYfMJxyOL5KnYO5Q5ZTTq34qintTGO3mYIlckvx/mpC/Da5B1h0Ij0efdxYd0OUU8zRPi4XqX8xRTlT+7W7CjPvGGD/VhLx2AK3wPrWcMrZ+VuarjwHWHEOsOVW/VrZ5ZSTCYpyilO63cQTJHCz46tKee2Y5CKRofnkDunz0OwXvST9Da7QTjlTKuKVo5/SvUDOjK9ikZKKJWRBmXkscweBPodTTknFivlOOY6ivhl1Vco3L0jgY2m7H5mc/27gJZ93i0UboXeBfjyTTjmtCIBmIr5qNkl203dAKRf9sUh2AX94J3DmtdHOt1FiMdqmSFyO2CnHdF5KARd/qPl25buHiChHmGMsP4tEZuwA1wwuUY7EVwmCIAiCIAiCIHQMEeUIwhHA2auDhZW3XugelHnb137rP955YAp5I6ZqfNpwyimXMZihgWxxyhEE4bDFdMrh4hwPQLuEKM1SJcrh+ColyqgZX1XWjhmp3mpRBRf/q0Q5XfQ42Q0sPpUKRcVp5dDiGcWnGHDiy4AD24HsvuC8edu56BXmlDOwNLidrZIbJ8cKdq0w3VLaia/i9WZRjumwgViLTjnx1p1yKhUdI8bbWiVIiggu8M+1+KpCVolM0kFHmEB8lSqU1joHt90ejD5iykVq83xnuBmzZpMbb83lyXbKMY/l9CF93thOMZUK8JsvUmRUYVo75djnF+8XjixKqviqUl7vk3I53DmnXKY2P7Ke1sVs52b7ZDFLYUo55bBzD4tyBoPzHV5DIqep/YZTTpOinKfvIVHMghNImDP6mBa3sKDKJNmt2zLPjwWJ8bD4qmndDlyYTjmu+Cre78NrtCiHncECTjnjWnxj9i2xWHUkFdNrOFfy+t31ReAzJ+k+PeWKrzIEPNyHNEMjTjnLzwLWX9ncfE165uvHnXZQaNsp5wiKr3KR6iYBXKWs+8IoGVwxu4ImLx2NA5DLKadVuN9wnb+CMJvMWwm84cfV3+mN4BTlqHNP2rogCIIgCIIgCELkiChHEI4AYrEY3nf5ev//u5/aV+PdmqAoRxdyiqUKBjI0+DhVEFGOIAiHKbG4jonyLFFOJE45qojsiq/a+FZg49urP+MLPoq6aJ7qdTjlWKIcvjvbS1GB6bq7gdPfoB1JygUdXwUAg8uB5Rvpse2W48dX1RHl9CtRjh230ywsypl3DP0fi8G/g70tpxw7vspyY6k0KbyqVCynHFPswE45NUQ55RKACh2jRIoK19MHmluHRvEFYaoIM1fiq/JZap+JrhqinAbOwez+ajEZQIKUuKcLSLWIeGmtAAAgAElEQVT2b36iNUGZ7ZTjx1cpp5w+JbywRSkTe4CffAC476vKwSJDYgv72LDYhqOOUj36vOV9dt9Xgc+c6D6uU/upbfcsAHoXBl8zxVl+3FMOKBW1SGj6EIlJzCIcoO+yH3uytlOOl6SiuSu+ate9wJJTKeqF58/vY9GbidlOWODCQrZ4gpZVsuOrcrR+oaIcFYk1fdAdX8WCl6HVwNgWOu99UQ475fTRuc7L9J1y4tqBDdDti+ff5xDlPPy/wMEdwN5H6X+X0wYX/MslHYHXDLzetUQ57TJbTjmtRFDNW0UCqSgikOYiyR7gwDZgYDlw2utme22iJzOg+9126JkPIBZsu60iTjnCkUiYU87V/wqc+trZWSdBEARBEARBEIQjGBHlCMIRwnWXrvUfX3PGcvzkXRfU/Uy+qAtihwynnLHJvC/SkfgqQRAOW3ynnIIhynFEE7UKF2Tt4rOXAo5/EXDyK6o/Yy6/OE0Fx0Q6GDsDGK4K7JSjCupcDB1YRoVvdiSx46uG1wGLT6HHu+qJcqx9USpSQbt/SXA7WyV3kIrcQ6vUE2Z8VYROObbwoyWnnJiOd8mOAZt+SI99p5waQlU+hlwI7hrQrh9RwwKGVK9yS5orTjlTVDBmtxLGjxYLET3ZTO3XkUImHFvEQrgwp5xyifZRK22X15vPDxY8TOyhtsznhS3K4fN/zyb9+XiyWshVyNIxYwEFO+UAen33bAIm9wIHn65ev8k9NO0Z0SIQFi64nHKK08opR931Pv6M2i4rvmpoDU33bSHxE+B2ygFoXvls9fOTe7WYL9mjtxfQgiqTZLdav7LefxxJFveUKMcVX+Vw3fHnqYSKZnyVKcrh4vrQGiA/Tv2HLcrhftcUFAFGfJXllLPibJq6RDl51Zfvuk/N2yXKiQOIKSHQdPNClEbiq9qFBWCJLt1/d4p2RTmnvwF41/2dX8/ZgvuLF38uGvHKXOM13wYu/ED781l+FvC+R1uL9bHx1Pks7iHCkQQLQO04y1NeReJ+QRAEQRAEQRAEIVJElCMIRwixWAxPffwFuPHdF+BVZ63AcYv1IG1/l/tu4ts27/UfT0wHC42b91AhYmzCKhQLgiAcLsSUW0qpQaecZx4APn0iMKH6xps/Cmy5JXz+XJRO9wGIGU45Ne7O50JtuUgxORzTECbK8eOr2CnHcihgp5ySinPhAubIeiq6D64Adj8Y/AyLWXif2AIlFjoMLKNpu4KP3DgVDucpUU5k8VVqvbmgHokoxxCN3Pc14Fu/T44tDTnlqNe4yNHVrwUGUcNigGSG2oTdfmaLwmQTTjkhwrhinoQMxelqUQ1HIMU9ID0ATIU45bCYohWXJ19cpc61wRU0PbijtlMO///sIzRNZpSoxBFflchoMV+qW5+3vL2Tqg86sK16/fi13gVA38LgOprtwBSzlIz4qolnaWo75cxbScdm35agiMpFqtcdX1WYJlEMbxeg+0mXU07ScAji9/E549nxVabIqOh28QFoX+Yn6BxJO+KrTKccANj3pHZlMp1yAC2qY5FSnF242ClHrfOJLyMnsEUn6+XYTj4sykmGFPW9JG1XfkK7GjVKtzqWnXTK4fiqVkQyzdJufFU8Hk1k0VzlzGuBF/wDsPqi2V6TzrDguOjasimUa4d4gr4TwvodQTgc6RkmV5yTr5ntNREEQRAEQRAEQTgqEFGOIBxhbFhUfcfk995xrvO97/nWA/7jcUOU48VjeN05xwAAPv2zx6NdQUEQhJnCdMrhwp4vCHCIQZ66nQrvo49TEfb2TwObfhQ+f18Y0U3FY98pp0YR0Y+vYqecdNANguFiMBeH1z8POPsdumjM+E45BSXKUcseUe5p84/TsSkMi1niSfqMHV/FIhd2BCm26ZQzfYi2Y+lpJEZI90LHV7UhyuFife6QivDi7WpTlMPHiMULuXHDpaOGKIdf82OPBnQUT9SweCPZTVFZc0GUUypQW0p1VzvllM34nzrxVab7DYsleN+WS/r4ZAbDnXJy6lwsONxc6uE75ajjmJlHAqAD2+l4dg8Hz3eGRSqj6rop0aXOL9spZ4rEKCyKSfboSBQWEfminO3V6zfBTjnzKaIHcItyeDsKkwAqhlPObr1dJok0CfH2GfFVNZ1yHPFVxSk6xwG9TXwMKiWHKIffM6X3X86Kr+L9x/umqJxyasVX8bo5nXKUKGfYcAbidsb7xBflsFOO1X7t+KrFpwDveiDoLMDr56VovtMHVR9trIsJ98X5bPMROfNW0bYuOqm5zzUDRwDNhNgl4JRzBItrWuW4F5IwR5g54km3y5UgHO6c8qpq5zxBEARBEARBEAShI4goRxCOYH73/12BTR+5CvN7g3e1Xn78wqr3fuqnjyFfLKNSqaBUriCdoO7hZactm5F1FQRBiBwunhZz2h3BFMXYjD1B0+wYMH1AR0yFYQojvJThlBNSdAV0oZbXK9GgU8789cBVH6t2rmBHklKBtpHdMOZvoOmCDcDo5qCYhEU4cY+K7vVEOYUW3EbsbUn3Ace9CHjfJioeRxlfBVAETcCNxdPuFo1iO+VwLFZ+ktyWAGoTYfP1RTmqraUjcsrZ9ENybTLhtpZSbW8uxFfxOiW7azvlxOvEV5mxVVP7gC23Ah9bDkyOKYcUdQ5lBuk8da4Li3IicMoBSPSyf5tuy6meaqeYgvqfRSSJLiW4s0Q5LqecpO2Uo9refpdTjnqtx3TKWanW3RFfxcKSRJrati/KcRThhlar+CoWuIWJcrrrO+VUiXJc8VVKcJGf0OI/P74qSX92fFUjTjkMR/uYfTKfn4MraH+MPUl9XnrAOHdVP8r9cFXfYsVXuUQ03E6XbwQWnKDeVyP6Jq5clfKTzUfkdA8BH9oOHHN+c59rhh4lymnFuaZZvITuh2dieYJQj7hX+/wVBEEQBEEQBEEQBEGog4hyBOEIpr8riUzKQ38meDfxr54YrXrv6EQe/3bbFhRKVCBNejGM9NJAeKlcQblMz3/pti34jLjnCIJwOBCLK6ecvC6w+2IQhyDAF+WMaueEWmIHLsJzTE0j8VXsFFEuktAjkaa/KlHOBIBY/eJsoksV8itU1F11EfCK/wBWnkevzz+ORAL7tujP+KKchHJnsARKLMrpma8EH2065eQOkUAlFtNChHpuKY1ginKyo0F3j7biq+J6noAS5ZiiphC3HF/MwfFVA9r1ox0evQH47X8En/MFYRxfVcPBZ6YIuPdYTjm+qMEzzoGQ45PdF3y8+3fUBif3qGPETjnzwuOrfKecFtqu7ZQDULTT6GN07NP97vimvOXKk+yic9IWvbFTDt+ZnuzWbiCNOOVM7qF9kJkH9C3W62euO2CIcpSwJJ6k/iLMKQcAhtaQSKUhpxxr+yuVoFOOHV9Vdjjl8Hu5zwEMUY4XFDXx9pTqOOUkDVFO2hLlxDz9HeAlgaFVwJ5NtHzTKcB3yhnX687rFItrMaF5HtqwaGj1xSSqBGo7bcSVQDI/qcWVc4lejq+aIecaFlfNRFyWINTDE6ccQRAEQRAEQRAEQRDaQ0Q5gnAUEFMFiFOWDeCpj78AbzxvlfN9f3/TYyiqIlnCi2N0Iofr730aaz78Y6z+8I8BAH99wyZ85meb8cCOkEKYIAjCXCHuqUJuxSjK1hCDjD1J0+yYFmQ04pSTsuOrGnDKKZeUg0/a7aaRG9dCllqYUS1egv5OuFp/boFyzNmzSX+mnihn/1M07VtMBdhijj7/qRN0Qb8Z2F3ESaeccmKtiXLini6mszikMGmJHcJEOXZ8VUROOcWcw5XFcOhIpLSTTzPsvBd45Pvtr59znZRTDosXAsemifiqqX26zRVzQYeUrhrxVXklpmhFUMb7ssop5yl67DvlWPFNdlRWIkPno9Mpx4ivSvUYTjlZEitx/3PA5ZSzF+gZIdesDS8ALv0zYOnpat0d8VV+/5CktpJTbdIpyllN7kPsxhPqlOMQJfHyQp1yStUin2QtUU5CuYjZ8VXTNK9WnHJsweTyjcCOOx2iHPW5vCO+Kh7X/5tCNJuR9cCGFwInXwOMHBv+PoajzvITzTvlzARdg7QfkzMkkmFnJy9EfCUIM0n/Eu1IJgiCIAiCIAiCIAiC0AIiyhGEo4SnPv4CfP86srV/92XrQt/HTjmJeHUhuGLEjLz4c7+KeA0FQRAiJubpomnCcEoAdFGVyU8Ch3bS48kxXZSu5ZRjxvWY8VWJWqIcjs8qBuOr7OXUFLIYJNLaFcTlHDFyLIAYsPdR/VxAlOOIr9p+JxXne0aoAFuYAnY/BBx6GtjzSP11MikVqSjfNRB83hdmRCjKqXKzaNcpRwkF8pNBsUOYUw4/z8ehawCYdjjlVCrUxhqllCdxiSmeMgUwrcZX/eYLwA3va/5zYfA6pbp15Azvt6ZEOZZTzsSz9LiYUw4phlNOWHwVnxPlYvMuQkW1zmZsjlmM7RpwO8XYIp1EWgktXE45RnyV6ZRTmCahUaUMIOZ2ypnYSy5WAM3jwg/ozzvjq9gpJ6GFRvGkW/gxvIam7BpW0ynH2l4WQPG6VIlyKtVOOSzwmDQcHFmU4yWVYJHjq4xpuRi+bgmXUw7HF1qinJXn0nm+87dA93D151j0VQ5x4cobbmk26V7gVV8nQVcjTjle0nDKmYOOHLEYRVjNlHNNosu9XwVhNnjBp4BXfnW210IQBEEQBEEQBEEQhMMYEeUIwlFIwovjM6881flasUSFhqRX3T3kim3EjAiCIMw0cU8Xius55ZjxTtlRwymnhtOG6ZIQiK+qIcoxRUHsmGG6QTC5Qw2KcgynHLvgDFBxd95KyynHEK/YopxyGdh+B7DiXD3/4rQWP0zsqb9O9nYA1dtSK0asUcx9lh3VAh8WfrQqymHRB4sJ8lnLKadY/VnAiK9Sxz/dT64rBctt6YFvAn+/GvjsqcB9X6+/XjxfUwQSiK9ytJ9GKObIdaUVQY/NPV8h4RavExfu2d2EjwULpoBqYRxjxldN7ddOOaVc0G0lo5xyXMIuUzDSbISV75RjnMeDK/Rj3yknJL7KF6VkVHxVPaecbi1OKU7p6KqR9cD4M9XH59mHgeG1wedYdOJyymFRjpfUQqPuIbcL19Bqmo6qmNJ4yE/FVE+1MxC3c94WFv2Y8VX2/Fi4kzVEOdxnxBNaqALo41Jix6QQB5WAU44SA/pOOdZnVpyjl2mKcrqHAcR0f8dtjNtv2XLKqRfp5Dvl1HDAiXt0zEq5uRlfBQCDy90OS52Aox0FYS6QSItITBAEQRAEQRAEQRCEthBRjiAcpbzkOUv9xz9+5wX+41KZCg+ewylnuhAsoBVKItIRBGEOYzrl2KKcckkXqwFgdDNNkz3knGA75fz2P4H/eGFw/lP7aRksjGABQqPxVaU8uep4qWAxHWjOKceMp3Ex/7gQpxyv2sljbDM5law4W82fRTnKvaJpUY7ax+w84cPfMVHGVxluFnEvXPQRRqVMQgXbgSM/QYVyLvaHOeWwWMePr1KCgJzllsOOTIUp4N7/qr9efgyRKcrJkutJ3FPtpwVhDQt5xp9p/rMm+Szwo/cAP3wX/Z/s0cX0giXKicUac8rxUiTYMEU57JDC51BmnnYWsTHP7VoRdC5cTjnzDKecdD9tYyEkUowj4xJdKr4qxCln6enAuiuAxafqtlWY1qKcZWfQ9MAO/dnxZ4GD24FlZwbnyW3O7Ef4+PpOOYYoJ0xYMW8Vbdu2X9P/oU45rvgq2ymH3X/U+yrl8PiqgFOOKcox+kY+D4rTQcckm2QNUY4tXBxaDfQupMemKMdLkBsRuzSZfUvM0223kKXtDRMvMf1LgFRfbQeceFL3s3MxvgoArv4C8Py/n5llJbpmzpVHEARBEARBEARBEARBEDqMiHIEQcDBKV1gHM9R8SjpuUQ5wQLa+HSIW4AgCMJcIO5VC2W4kPvoj4BPrteuHGNP0nTpaVQg5uf58888ADx9d3D++7dSsT7uBQUxYeIYQBdvy7ZTTquinK5gPI2LBRsojobFBoH4qkRQHLH9Dpqyg0Syi4QCXCyejMopp0Z8VbkMPPQ9mtbCjHPKjlYLP9p1ymHyk7TvuFAe5krjO+UY8VWA3nf2+5Y8p1rYUWu+pvtL0RAJJdL62DYDC3kO7Wr+syYszuD5uZxy/PgfY//y8Rl7Eji4U88vuw/IDNGfGV9VylGb4M93DdLUFWEVcMrJVr9ei1IOQCx4Pg0s149DnXImabuHVURooqu2U07PCPDa76iYOCXWMJ1ylp5G0wPb9Gd33kNTW5STcIlyWMxliPa8OqKcRArY8Hwt1AoTvvD2m+eo7ZST6AIQ08LISqk6vooFPKZTDrshxWK0/6pEOXklymnAKYf7Hd4/tmAyFqMIK6B6n/Qt1G3Pb7/ctxhOOY24Z8RiwJpLgIUnhr8nngCmVFueq6KceccAA8tmZlnilCMIgiAIgiAIgiAIgiAcQYgoRxCOYq5/+7k4e/UQzlkzjI+/9CQAwHP/4VYAQKFUXSjNFYOuAw/uPFj1HkEQhDmDWQDm4h4/t/cxKtYf2E7/jz0B9C+lmJrsmBFfZbgzFKeDIpKxJ4GhNfTYLPZ6NQqJvlNOkebtpahYXhVf1YRTDhetw4rU84+j5e1TwiMuKMcT1fFV2+4gh4hhtV2JDAkFbKecQ7vcDiU2LBjqspxyasVXbb8D+O4bgKd+WXveLHZI9SmnHCsiqVVRju3mUciqSBlVKC83GF8VJsop5khs4BJ2uCha4gp+zhQa2KKuRuDPtCvKYdHLinOovfQvMUQ5at19wZQRX8VCh++9Gbjpw3p+U/tJINE9Dzj0tBZ2FTm2yIiv4vfb5ExRTrNOOTk6r8x4p65+Ldro6g8X5aR6dARUsst9bhemq4UcSdMpR/U9S0+nqSnKefpuajuLTw5+3nbKqVT0Y1O0x20mMxS+/Se+XD+2RTT++nYDqATj/WynnFhM7SfVPirlapGP75QzFnye+zJz/5WMvrhSwymH2x7HCgLh8VWAjuoznXIAoHeRdmny+4aYcuFip5wpLaiqxyu/Clzx0fDXPdMpZ47GV80kia76sWCCIAiCIAiCIAiCIAiCcJggohxBOIo5feU8fPMt5IZQstwKbt88irv/9LLAc9vGgnebv/4rd3V2BQVBENrBLChzcZYL7exGwdOxJ0iI0j1kxVepgj4LD3yRQQXYt1UX4ANOOTXiq1jwUSlptxNX/FBuHEg3UJg1XSHCHHpG1tJ031aalg1RTswS5Wy/g6KreD/ZTjksyvnSZcCtn6i/ftN1nHJc8VUssjhUJ1aJ17tvIR0v040lFne78NQizCln+iC9xoXyMKccFglxRE4tp5xEunFRju+UMxl8jsVf7cZXHdpZ+3314HPjrLcAH9oO9C7QIriiHV8Vr46vmj6kz0OAjn+3csrZs8lY37x2UQG0SGYqaqecvFtYN6girNL97vimQpain9Y+l1yQehcqpxxLxFWcqo7lYfFBIav2RQxYcAKdoywcBICn7wEWnVQt6rFFOaZIy3TK4eWGOeUAwJpL9euh8VVKoBaIVLOccgBaT3aDKjuccnxRzt7g89yXJTMkfKlUdN9bUjFmYevG22hG5oXFVwHkYBNPAMNrg8/3LgzGV/HyzL6lkG3MKacR4p7uKxoV+hzJiFOOIAiCIAiCIAiCIAiCcAQhohxBEAAASS/YHVx63ALM7wsOhn/hVnJZOGNljWKOIAjCXMEUV3CRnQurLLqZ2EMF1rHNVJTtHiEhARfCuRDMBWd2g5gcBfLjIaKcWvFVllNOIkWFx3IxGAWTGw8WlcMwi5ZhTjnsADG1Ty8bUAKUhP5/6gC5ciw9w5h/F+0PU5QztZ+EHKaDRxjs0lG1LTWccrjQzwXxMHxRzmIrvkq5sZRL4Z914bthWJfHLBLiQrkdR8SwyIWPP2+zyynHSylhRwOCEd8pZzL4HLueJFLhQqFG5huVU06qx1gn2ynHFEyxME0JG0o5Ld4CVHzVPBLmmGKN4rSKLbLiq+o55RRbccpxCOsGV9DUjK8yz9n8JJDqBpafBbzlFhJreAk6NrvuAz6+kpxXXE45iTSAGK3r5F46ZxMpigra/xS9p1wCdt4LLDsDVXCb811lDFEO74t4UotT2GXIRSIFHP9i9ZkwUY4SqAXcmyynHIDOGT++qlwtpEla8VV8THm5viPPdNC1rFwM7+9YFMSiOMBwzHH0zSPrgPdvBo45P/h830Lq78rloKDI7FsiFeWYTjlzNL5qJjnnOuD8d8/2WgiCIAiCIAiCIAiCIAhCJIgoRxAEAMDvnbwk8P9VJy4CADzwl1f4z/36SYoXeN8VxwIIJjsIgiDMOcwCMBejubDKopzJPVQInT4IzFulBSz7ttC0aIlxuDDMUVDDrviqGk45XGwul0mMkOjShWIWe5TLJPhpKL6qAaccjqrJGqKceEJFsSR0gZkFNGaMiy3KmdwD7N8WnF8tcupztijHj69yuNnk1XrY7hk2JSXK6V1I8TcV2ymnxfgqW4jAog8ulIcJYHxRjhVflTtkvS9nOOVM1Hf0KTniq0o5wyknrdtlM0TllMPCIlOcUNMph4+9Ol7FvG4nAInHuoeq3VyKyimHxRj8+rTLKWdcP47KKWfhiRRp5KnoMTu+qZCtdjiJJ+m83vs4refoZrdTTixGzxWmqN33zKfnF58CbL+T2sieTeQ6s+zM6nWznXKKpihH7QsvoY9Ld434KgA4+x0kzBlY7n7dd8ox9q3LKcd0g+LzyyRhxVfxdvMx5v2Zzwa3rVwOF+Xwvu1yOeWECReHqi9qexdRe8uOBaO3zL7FdcxbJZ7Q7Uniq4D1V2hxmCAIgiAIgiAIgiAIgiAc5ogoRxAEAEAm5eHGd1/g/9/fRcXdgUwSZ68OFm9SCeo6jl3YQMFYEARhtgg45XB8lbr0yRnOLyxKGFgK9IzQYxYMcKGZ3R54yqId3ymnQVGOH91jxVcBRkyREl80JMppwCkn3UevTVmiHID2EYtyuHieMorMyUwwvmpylKK+gAZFOeyU00R8FTt7NOyUs4iOJx+ruNeGKMerFnP5opw68VV8/LitdYU55eSVU44SdhSmUJOiI76qmNduLl4q6IzSKPyZtp1y1PonDXePKqccdZz52AD6+JTyup1UKsopZ0iLyfzlKAGIH19VxymHz6tCRE45578beNtt9NgV35TPVjuceEkSj/E5Pf4MtVuXu0pSCeAmR3U/tO4K+szuB4GnbqfnnE45dnyVIdLKG0453F/Uiq8CgPnHAtf8V1BgY+LafhZgBZxyMvq4lUtA3Prp6SVovVhExdvNMVPcFxWyev6lnOrDWoivquViZtO7gKYTu4MuP7G48f0wFewv28FcN3HKEQRBEARBEARBEARBEIQjChHlCILgs2FRP1591nK84+I1gecvOXZB4P90Io61C3rx6O5xrP+zn8zkKgqCIDSOKa7gYrRdyJ3Yo0UJ/cuCLjFeigrBFUM44TvlbKH5c6SNKfrxQsQxALkxxDwdX+WldMGYxRdhQhYXZnE/HlJwjsVI4OA75ZQsUY4St/iiHMOlwXTKiXkAKhShA2iRTy1y47QsW4TAwgxXxFS+UVGOEsf0LqRpdkzPuyVRTqXaKad/id5vXChvNL4q1Uvzm67hlAMEhQ3O+XK7CHHKScy2KEetfy2nnLLhYsT71xTlTB+i/Z+foP3bPaTdXLit8DnIn0/1KrGZyylnQruuNBtfZe5bk0RaCzVc8U35iWoxBZ9ffIw58s12ygHIcSU/SVFOvO5rL6fp5puAe/8TWHwqOXrZxBOg+CsW5biccpJ6u+qJcurh2n5fnGVsW734Kn4PT7nPs51yClm9beUiHaN6ohynU04Topw+cozExLNBQVHcM5xypiJ0yjG2JyqhjyAIgiAIgiAIgiAIgiAIcwIR5QiCEOBjLz0ZH7xqQ+C5t160Bn1pXWROJ+J4Yg8VYvLFMv72x5tw33bHneqCIAizienKYDvlMJN7gINP0+OBpUFRTv9SABUSW9gxVmNPkiCH5+ulgtOa65Wgonm5EIyvatcpp5YYqHvIcsrx9Lr4ohy13JTleFKcpgimecfQc0/fRVMWwdRi+hBthx0NE/fIWSU3Xv0ZFjBM1Imv4vX2Y6LUvFoV5ZRLWjTF9C91x1c9+7B2LvE/b8VXxWLk1uF0yjFFOROoSSnMKYfjq1LBuKJG4fmO7w53/2kEFl2YQgLfKceOr4pVC7KKOXIeKWT1vjadcvpUvCY7rvgxQjGga9AdX5Ub18KWRuOrfvExYNd9QReiMFyCqlrxVbwOB7bT1OWUs+Q5wGM/AQ49o9e9byE9f+fngT2PAGe+yZ0dGosFHZPM9hBwylHb1bYop0GnnEB8Vam6/wW0iCfZrfcfH2M/vmoyKK4y3b6q5qeWz/0C0KJTjhL7jT8bXPdYXLfdQtZ9LFvBFAxJfJUgCIIgCIIgCIIgCIIgHFGIKEcQhIYYzxX9xxxfxXzxl1tw9b/8Gndt3YeD2QKeORiM4tj0zCE8vb/BopggCEJUBGKIlIDBLgpP7KX4qlgc6F0UFOUMLKNpcdrtlMPRVUBzopx0HxXeASqS87qVbKec/urP2piOG7VcIDJDQFYJHgLxVQkdxeI75RiinGQXCXJKeWBkHT23636aFqcpsqcWufHw7cgMhkQPqe2v65Sj1tslyol7eruYwhTwy0+GC1gq5aCTS3qAjtWU7ZRTBG79BPCDdwY/z8fPPA5dA7T/7PclUnp+9UQj3ObytlOOGV+Vq/5cPUp5ta6V+vu6Ftxuki5RDsdXsSjHiq+qVLSYafqQdiXqHtLCkb5FtI12fBVQow0ZTjmNxFdl9wG3fhx46PpwpxyT0PgqS5TjJUnwxMfuwA6auoQcF76fBEaFSb3uALDuShLApQeAE18evk5eSourzNupY44AACAASURBVPbADkteQh8XOxqsWVKGWIZxOuUY8VWVstvdhvdFqkfvV8+Or5qqdoNyue4AWqwWiK9S8wsT8rhgUU5VfJWn49gKUxGKcox1k/gqQRAEQRAEQRAEQRAEQTiiEFGOIAgNcfP7LvIfl8oV53uu+dc7cMpH/g/nfOzngeef99nbcP7f/aKj6ycIglCFWQD23UvMQm5MOeXsJEGOlyARBYsq+pVDRzGnC86FKSrIVolyksFpLZacCmy/gx4nuvS6cUGdRRwNOeWYopx6TjmGKCdmOuUYrg+AFV9lFJyH16rPFwAot456bjm5QzVEOfPCo4cAEsPUcnApqfXg/cSinLgSflSs76ptvwJ+/lG9721YlMOikcwgFcfZkYeXwyKLScvJx46v+v/Zu+94Sco6XeBPd1Xnk+ecyXlgEkOaAQYQyRlBVrwLCGYUd3WNi4IB14TsGnfVFTGsCVlMawIBRYIIQ5TMDDPD5Hzm5NM53D/eeqveqq7qru7TJ87z/Xzup1N1VXV1nca99czzA8QIHWdTTsHZlFNhfFWxaIVWvJpy9Ej946vk+LWRjLAywxh+mnKC1rlXKjrGLA1YAaiYMr6qeaZYnwyAqee55zk0aI2a8tOU07tF3CZ7jCajak05buObhkX7kyoYcoyvMppy3MZXzT4WWHq+uJ/otJ4//Fxxe8ybKo810kLuTTkyoBMc5fFVZlOOGspJWL+Z8u/LSZ434SalKUe33g8Y46syMH931GWc9BjQOg+YvsJ6rp6mnHBc/HYN7TfGV8lQTlAJMrq0I9VLbS/zE+4kIiIiIiIiIiKiSYOhHCLyZXGndaFp4bTq/4K35LwYSkQ01tQAjq6MFJLaF4pQSd82MbpKvi7bclqM5/JpZXxVRrwnM+DRlFOlYQMA5qwR7Tzyfeb4KuPiudmUU+v4qkpNOe2O8VXGBe1AsHx8lXqRWW29kE05ANC5VNzKdXrJDIpgiptohZYTyRl8UcnPIQMCMswUCIrv0Tm+SoY63LYJuIRy2u2NFWZTTk4EDTID9hYW11BOm2iAUeUz4nwM+RhfpYZW1FCOsymnVAQKedQknwU6Fon78nyshwy9qI0h8rw0m3KMEIN6fEtF63VAnCuyKSfWroyvkk05xudXw3aVziH5d5z30ZTTI0M5B8WxrWd8VXa4vOFE040Ql3GM5Kg8r3aV068Xn7VrmfXcnNXA678FnPbRyvukjq9yC2lpozy+KpcSv7nq+R+Oi2XUpiQnGeIJx63QkRnKiVnbyWfsAT+31h1AjC380AsixCTJv5VKbWJummaI8W7q+KqgMhqvkeOr5HELJdxHlBEREREREREREdGkxVAOEfkSUC4QBIPVLxZk8sWqyxARjSq1lcFsylGek00Ke56zAjiAdTG/VQnlyOBBPmUFGOR4K3X9fpoY5qyx7tuacpzjqxrYlBNrF4GHUslofVDGV5mhHJfxVer6W+ZYzTlzVotbtSln51PAwc327WYGvUexxNrEuB5AtFEceMW+H0DlsUpmKCdubQuwgh/OUI5sdKkWypEX+2Pt9uYTGVYq5KzzIdltvS6/P7XlIuLWlJPx35SjjiGytZJkrOCL8/zxq5AVwTRghE05SXGeqCEJr6acoGN8ldqElO63vpu4syknYn1/6nZi7dY5JOUzIjgVbRHHOWcfqWlK9QFP/sBqvgLE+ZzPVg/XOduZ8llxPjqbbIIhsS/yu5Pfp1tTDiDacq7fASw42XouEACOvdo6Hl5soRxjO7YRfiFg/kmijWek45H0GICAvYUony4PqMjxVWpTkpPZlJOwAnby98k2vipj/030CuW4MZvMahhfBYhzb2ifOEcCalOOce4Wc+XtSPUyPzNHVxEREREREREREU01DOUQkW9/+JdT8OB1pwMAXvzMebjl6tWeyw6kxYW2ojLqKsugDhGNJdv4qkj5c7KNIjtoD9gkpomL6Yku8TgzaF1Uzmes1gv14ql50dfH2JHZym+nHrHaK8zxVaPQlBPvEBe1c0kjzOIyvqpaKCfaZo0EksEi2WxSLAC3vRH4y+ft23VrD5Fibdboofs+A/zvlcZ7BoG4Mb5naL/3ZyrmxWcOOUM5mvh/8nNJMkDg1e5jNuUooRxbU06TtV0Z9BhWQjky3KS2cURbrQYfKZ8V37mfUI46hkgN5RSUEUvyHFADPNUUC6L9I9Eljt9IQjlZl7aQsqYcJZQRVEM5alPOgNGMFLDGV116C3Dsm42mHOOYq0GTmEtTjmxaCjeLpievUM6ztwN/+BCw++/2UI6fppxoq7iVgSvZ4uMMaGgh0WDkHKFVqV0l5BHYqUYPl4+vkvsJiPNy6XnAm+4YeRNLMCjOX2dTjjNsFEqIfZL7FXQL5UStZT3HVxlNOWrrVqUQopM8H+tpyhnaZwQZjX2Xvy3m2LYGNeXIfWMoh4iIiIiIiIiIaMphKIeIfFs1pxULjNFViYiONQu8/9X2nj7xr+OzBSuI8/pv/W10d5CISGVrygmVP9e1wrqvNuUkukT4RLbCqBf982nrArwtlBO231bS1AW0zRf39Uh504kMqoRrbcqpNL7K+L1O9rg05chQzpAIL6nhHvWCc7TVCuXIYJEM5ex6SoRdnM02uaT3RWZ19FDfDmBgj7ifGQKmLRH3q4VygpoyvmoETTmlEoBSeVOO2nwiP4falDPsaMoJaPbgQbS1QlOOsd8Vm3I8xlfZmnKM7ytfQ1OOOWorLAJQauNRrXKp8jBKICA+Y9445kUllCP/BosF+/iq9ID4vuPTrEaTY660mnKybuOrWsX3XlS+66wMtTWJkIfchx1PALeeAdz5EfF4/8vidtdTVihn+KDYp6pNOS0AAtZ3awb1nE05utGU4/iOvZpyRsKtKUcNsfhp8apFpMX6rQLcm3LMFisjKOU2vkq+J5ywljdHORmvpfsBlBzjq2oI5dTSZKZqmgEM7rOPr5K/LQ0P5RjHhqEcIiIiIiIiIiKiKYehHCKqW3PU+4KIDOCooZyX9wx4LU5E1Hi2cTrGRXbb+Krl1v2W2db9U68DLvmG9R41xJFLWxfgQ8oFeHmxt1rDhiSbZtzGV219GJi+0t+oFbUpp9I4Fzn6JtVjjX0CRIDEHF/lEqCxNeW0iovUQR2YucpaHwBsvFfcqiEVQAR9vMa7xNpEYCKfEe+TbRjZIaBjsVim0viqQs59fFXQK5Qjm3K8QjkwxivJUE6bFZwBrGNTzClNOQeU/cmWh7ISnaIBRu4bYIxH8tmUYxtfpSxXUEYsydtaxleZY5Qi4twYUShn2D2YoDaplFxCOc7xVZlBcTxl8EulNuWoYYxoq1iP2iJkNuU0ifM3lwI2/hn4/jnA7qeBl34rvu8D68Vyu54CerYY7+0X+1zt7zgYtI8mM1ummuzLaSHx9+X8jhsV5HBuSx5PeS7Yxj01OJTTvgDo3WI9dm3KMT6n/H4qjq+KW78VzlFO8m9W/TxuAR8vagixFs0zxPmd6nOMrypYvyehuPf7a6GxKYeIiIiIiIiIiGiqYiiHiOoWDWnobArjX848DG9/zUI8c+M5uOeDp5qv9yWzHFlFRONHvWgrL0ibF1Y1oGOJ9bo6vmr6CuCws6wLzGlHG0TOZXxVsIbxVYAVytHC9qaTZA+w/RFg2YX+1qNeBK/UAlGxKUeGclxGTTlDOQtPAZaeL8Ic0VYrzLHxT8b6naGcCk05sXZxm+oDhvdb+5cdFm0pkRYfTTlu46uqNeX0oYwZGglYTTde46sKyvgq9fMW8uXfQedScdu9UVnOGI8k99vP+KpgyB48ySsjlpyhLj9sTTkdVuNRJXueAw68Uv58LlXeEAOIAEXaCOOWigAC4vjKv0G38VVD+62xcSo9ajVUBRxNOYC9jUgeJ9mUk0sB2x8V58SZnxLBn8E9Vihny0Pi/GsxfgOS3dWbcuS2y8ZXuTXl5MU+Rdvsn6fRNJfxVRFlfJWfkF8tOhZbDUOA0ZTjMr4KUAJzLkEaeSzCTdZ5ZI7X08R3IUM5tvFVNYRyAgH7b61f01eK211P2vepVFJCOY1qynEEkYiIiIiIiIiIiGjKYCiHiEbkyU+eg4+cuwyfvvgItMXD6GyyLkj//MkdyBUYyiGicWJeRA1ZQYuAEriINFlBC3V8lSQvMDtHtLg1JNQyvgoADjtbhA86FtmbTl65R4QVlvsN5ahNORUuOJc15chjo4vWB0AEB5wXhOUx0CLi/on/BFxxm7HOaSLMMbQf2POMOJYy9AOIYEAx5x7YAKyQQrLbCvcku8XxjTSLxpThSqEcI1wU1MSosawyIqdSKMctgKI2uUTbxPfYvsj+HZvjq7LK+CpnU47jO+haJm7VUE7eaLkJBsX61bCNkwytxDus8E6xKI6rPG/0ekI5xrJayPgefTTl/O5fgD99qvz57LB7W0i0xQpjOMf/yOfUkVvpAfF9uzXl6BGroco5vgqwh3LMppxmcc7mUmK9iU4RKgPE31m6X/zdD+wSz809zr69atRQjuf4qpC1f+0LredHpSknbB1Pt/FVjW7K6Vgswk3yvMylrJF/kjweMlTjtg/y3AnFrb+xoGOEnvwNjtQZygGMVjIf36tqwcniuKZ67U05xYL1e9KoEE2QTTlERERERERERERTFUM5RNRQ05qsCx433bWeTTlENH7kRVQ1KBMIiNv4NHGb6BLBDtcggAzlKOOO8mn3C/AyjOG3iWH6CuC6TUDbfHvTyYY7geZZwKxj/a1HvQjuuyknX0NTjrF+9eK+us7kQWDTfeLxyksBlKzQS1YZI+S6T0Yop+dVKxTTv9N6T9OMKk05Oav9I5wQ2wbERfOgVn181ea/WM0vMpgUCIoAzAdfAJZf5D6+KjtsrVsd15XqsUIiUvsicR52b7CeK2StII064smNDFnElFCOOXpKBsGM/+7mM/BNLqtFxLpTPppyhrutY1fIAwc3i/u5lHsoJ9Ii2m8AcbzKQjlFe5AoMwAMHQAS1cZXVWvKMYJAsiknnxb7nugCZqwCEACeu0Msc/SV1vvmnWDfXjW2phwZ1HP8/cjzM90vAnjSqDTlhFyaclrsrzeSHDEnR3+5NuUY54UMpaljAs1lZFNOonx8lXzetSmnxuaf130NOO7ttb0nnADmrRX31fO3VByFphzN2iYRERERERERERFNKQzlENGo8mrKOTiUwcLr78QvntwxxntERIcMeZFTD5c/J5tjmqYDzbM9xqoYYQd1fFWuQU05KnmxPDMIbPoLsOwCq9mnGltTToXmCHNUVK89lBPQrGabnMuoKXnB3Bk2AUSwKdUjwi2JLmDJGeJ5OdLJ7Ti57VO3MhKpb7u4DSfEOof2eX8m9XOoAamgJsJXnuOrjAv8v7oGeOg/xH21KQcAmmeIdajrlQEdGTQB7KGc3q32NhRAnHsdi+2fsZCxgjThhHWc3KhNObmk+K7UQA0wwvFVIbHudL8I2lSS6rVaaF74JfCttSKAlUu6BxOcoRx1/A8gRgCp46sG94gxUE1u46si1nGqNr5K7mOk2Rh7lbLGYkWagM7DxTgrADjmTQCMoN6ckTTlyACa4+9Htp+UikDbAuv5UWnKiVjngLwdSYilGjOUY4ywcmvKkX/7clRY2/zy9chlwony8VXydfk3GxnB5znyjVZzVS2WnGlsTw3lKE05jfou5X8HnMEuIiIiIiIiIiIimvQYyiGiUZUxmnI6m+yjrXb2iosZ1/3yuTHbl1sf2oznd/ZXX5CIpgYZsLA15cjxVUYoZ+n5wMpL3N+ve4yvyg6Li+1q88SIQjnGe7avE6GEw8/1/161caPSeBo9LMb5pHqNsU9KQMJsyhkqb7XRK4VyOkQoY8uDwKLTrLYhGVSRzS5ezQ9yfJU62kmGcuT4qqED5e+T1FCOeiE7EPQYX6U05WSTouVnYLd4zhnKkdR9l/fVAIg6vsotlAMAnUutz1jIi23J0Ee4qUpTjhLKAcSyMnChO8ZXZQaAvc97r0tljq8KW61RaiOU237khq0WmoFdoqloaJ97mAsQ32HaCOUU1fFVAes52egS1K3mnYRLKEcLw2xCUsMYrk05SkOTbXyVsd5Zx4jbWIcIlnQuFe087UpoptamHK/xVepvRKzd2N9Afb8T1WghK2wlw04yxBLUrePeKM5QjltTTthPKMcItYQTVkBHc46vkk05yu9QoMbxVfWSYcOA8ptpa8rxCB3WKqi2fhEREREREREREdFUwlAOETXc1y4/2rz/3YfExZqu5ii6h7IoFsVFtWjIupgynKnyr/Mb5Ka71uPibz48JtsiognADOVEyp+TIYfXfhg47wvu75ehB2coJ5f0vvhez8V2uZ1dT4lbGRqo5b3qPniJtyvjq+QFZt1qynEdX1WlKad/hwhmLD4NiHeK55M+QzkVm3KaxPoz/VbQwKmQd7+QHdA8QjlGs0UhY21zYJe49QzlKCElLSy2J9tfgroVQEr3i+CAayjncBE4KeStsIQ8T0JxK0Ti+hmNzx5TQjl5xzrk+f2HDwHfPdP6nJWooRz5PSQPei8v/wZkC01m0HpP1qMpJ9piLVcquYyvKlj7Ee8U5xLgPr7KqxFKBru8mnJCcSCfEt+TDI3NMv43yvQVIqhy3DuAY6+2jrFze178jK9yjmFKdIlj1eiADCC+y7LxVc3GfjR4dBUgvt9EF9Ajx5ilvZty9r8MRFqtkXUq+RsTilt/b17jq+TnASo3gzXSzKPFuaGev8VRaMqR3xFDOURERERERERERFMOQzlE1HD/cOxc8/5vnhEtBHljjNUTW3sAAAUjnAMAL+9RRoGMEq8xWkQ0hcmLtmpYJeAYX1WJ2ZRjXBAOBI2mnGT5xfdGjK86uFEEUZpn+n9vIGCFMqpdeI+1i3FTasNMUFeacobLWx/kBWe3UI4McwCiKSdhhHJ8N+UY6zSbcgJKU06TFZJQQ1Eqr/FVgYB9LJekhlVko8zAHhEY8QrlyOMR1MX4mmDIan9pmWMFkHq3iVt1RJHUtUy0yvRutQI1ujK+qlJTTsFHU448f/q2i9eSPd7rM9cr16E25VR4n/wbyDpDOT3iuLq1hcjxVfL4yr89eb4WstbnS3RZ34HX+CpJbUiRTTDO9qJwszguehQYPihCM2ZTjhHK6Voubk98D3D2p8WxkOvTfIZyMgPiPDPP9QpNOeGECBzpjjaZRtHC1vEsZMU569Y800gdi4GeLeJ+PlXelCO3n+pxb8lRlwk3KeOrdPvr+bS4P5LxVfUKBoET/xk47CzxOKABKFnfecOacozz2tlWRkRERERERERERJMeQzlENCouPNJ+UXnjfnEh7/Jb1wEA8kUrJPOZ37806vtz27pto74NIppg5MV73aUpJ+YjlCMvzKeNUEi0TbRBuDblyFBOHRe/1SDPjCNqb9HQowAC4uJxJbEOpSlHhnI0R1NOLeOrjDBH+0Ix+ifWIfZDhnK82kOkoCbaMzID4hg0z1SachJWEMUrLFLMKaEcY78DQSOUU2F8FQDse0HcFjIicFIqWe9XyUCRpgRgZFNO23yxzuywCNzIY+HUuVTcdm+wN9TI9VccX2UsLwNQ2aHyphx5fsvzvVK4RrKNrzKOs2zKcYaZACuUU8iK7atNOTmXMBcgWk2KeRGoKBWt81oGvXIpqwkoMc16n1tTjubRlKPp4rtXQzndG0Q7EWA0ERn7qoZyom3AgpPLtyOPhe5zfBUgjoU8t5xNMUFHKKepq3HNKk628VVZccxkSGa0AiwdS6zxVZWacoAKoRxjH9XxVbZQjrLOqBrKGaOmHAA47TrgzE+K+/I3ouGhHJeAIREREREREREREU0JDOUQ0aj4/KVH2h5/4sIVAIDWWAib9g/Zmmv+6fQlo74/fSmP8SdENHW5NeXEpwGrLrNaDyq+PygubMumlli7Nb7KeSFWbsPP2BsnWyjnSO/lvOgRf2GgeIfRlFNwhHLyIpRS8/gqI8Cw6DRxq+niGJnjq4xWlUrjWOQ4m0SX+G5koCTcrIxV8grlFKzPLb8PdcSM2/gqGSSSTTkAMLC7wvgqY99lSCOoW005MmQwfKBKKMcIiHS/4tKU0+SvKcccXzVkPSfX0TYfmHsCcMYN4rEM0FRiC+UYxyTZAzz8deAbq60wkKSuMzNkhXIG94pj5zW+ChDHS21hUkM58njI0WeAFZ5ReY2vAuxjpADgwCuinQiwN7fI8VXRFuC6TeJ3wEkeC79NOYDYdnZYhM+cwTj17zKUANb+E3DWjdXXXQ89ooyvyohzVv79jmZTzsAu0R7m1pQT9hHKaV8o/q5a54jvVo+Wj68y7yuhwbFqynEyQznG71ujQlYax1cRERERERERERFNVQzlENGo6EjY/5X5Na9dhKPntaE/lcPZX30Q2bw1viqTd/lX+Q0WC43hv6gmoonBOS4HEMGRN/4AmOkz/KJHRRMIIAIk+bR7eGUk46uCmrWvM46o/f16tProKkAEO/p3Aj2brWBDUAdKBWO0U8nlc+nAye8HVlxSvr4moxFt8enWc4lOEVIBxIV6oHLzgxnK6bSPw4o0+WjKySsjX4z9lscxELTab6RcEmiZLe7vfcF6vlIoRwuJ79SrKQcQ45F6t4r2Ffl5VNFWcay6NyphGJ/jq8zQihEWyQ5bgRkZVIk0A9f8CVh6gXjsJ5Qj16GFrMBP8iCwfZ34LBvusi+vrjM7aIVy+ndYn8NJjhrKDIqglmzDsTXlGPshgzjRNveWGvXvyhnGiLZabVbpfmBwtxLKUc69hBL80ULujVTyONfSlCNDOW7nuRogCieABScBR19Rfd310MJKU05GnGO6z9F29Zq2WNwe3CT+Hp1NObLFCwDa5rmvY84a4Pod1t9T23ygeZb1uvod6lFlXN84/e+6oNKUo0Uatx/O1i8iIiIiIiIiIiKaMhjKIaIxEQgE0BqzLgqp46vSuaLbWxrqhd3iIuqctlEaG0FEE4+8eFpPUEZSGzqibRWackYwvkp9f12hnIgIz1Sz4GQRSln4WuC4d4rngrq4mC6DIW7hinM/B8w9rvz5eScAV9wOrHy99Vy8U4RUAGWdFS4yR2UoZ7o9lBNOKGERj1BOQR1fJUM5sikn4N6U0zJH3M/0W6GiwQqhHLlusyknZAVSWo2QwfABoG+bGOHlpWm6CL2YTTlyfFW8SlOOEVqJuzTlONtcqjULua1Xi4h90GMi/HRwk3j+qR/aly9ryjGCSTKU49YWYoZy+sVIM9mGI4MbeTWUY4RhmlxGVwGOEXQVmnK6N4rbTiOUoyvNLW5jsZzqbcpx+00AHOOrRnkskRayjmchJ35TzKacUWqVaV8kbve/LG6dTTmBgHVcvJpyAPuxueY+4JQPWY9toRzlMznPg7EifyMyg40dReb8LSMiIiIiIiIiIqIpg6EcIho1f//UOQCAH73jBADAQ68cMF/b05827z+9zce/6h+hI+eIi4PxMBtziA4Z8qKtn9YLL+ZF7Yi4AJtLiwYY5wV2GcapNwCkhcX+di2vbx/9jHJZ9QbgA88CV/0cWHKGeC6giUCKDFnUckE4EACWX2hvikhMs8ZX5SoEfSQZJGmabgVPADHqx1dTjmPki9kApIkGIFUuZTXlAKKhAwFgYE/lUE4ooTTl6CKAAZSPr3IbXSXJMVXOQE24SYRTih6NcXnn+CqXphzJPF5+xlcZjSryfI13AEP7gd4tIkzz6v3WSC7A/h1klfFV/TvFbcitKadZ3JpNOUYoRwuJ885tfJVXcMbv+KoDG8St2ZSjhCbcxmI5mU05dYyvcjvP1ZDeaDegaGHv8VWj1ZTTZgTRul8Rt86mHMD6rawUylFFW+y/2WFHU446Sm48yP+uqCPZGoGhHCIiIiIiIiIioimLoRwiGjXtiTC23nwRTltafiGsP5kz7//iqZ1YeP2d+PmTO7C1u0JjwAhk8yNr41l705/xmpv/0qC9IaIxIS/eN6IpJxQVF/jzaRE2cYYQRjK+ChAX7zsPL2+a8LuP9V50lxeCZchipBeEE12iFQUQF60DWuVjYhtfZYRKwk2i5SjcJD6XV/NLsWDtv7w4bjblBO1NOaWSCNPEOqygQvtCsb+Du61QjGdTjssYIDmOZ3Av0Le9SignYbTcGP/tM5tyjOPt1ZYjl48roRwz2OM4rqGY+GxeISbbepXxVYA4LrueFkGn17xfHIe//9RavqwpR4ZydlnbdooaTTnpAdGeJIM3gYAVcDObcozXmjyCM1qlUE6b0pSzQRwXGRaR+xVt9RfOk8fZz9+xn1COGhxpZIDDjRYW53whL46rFlFChaMUyol3iN9CGcpx+/3y05RTiXrc1M80bqEc4zci1SPG7DWK/I0JNzdunURERERERERERDQhMJRDRGPmbScvNO8fHM6Wvf7RXz6H07/8wKhsW4ZySnW+f99ABrv6Uuh12W8imqACjRhfZVwADsXFRdN8RjR8OEMIIw3lRFuBWcfU995QrP6L7jLgUE9Tjpt4pxjTVCwYQYUmEcLwoo6viiuhHEC8L95hD5m8/Hvge+cAxSJQzFljeczxVca2nKGcQk4054RiVjtP61ygZRYwUG18Vdx9PFm8E+hcCjz8NRGCaKswviqcMFpuHIEaud+yfcepkAEQsEZBZYeUEVgubS6xdp9NOY51xDuAg8bop0WnA9MOt4IWgH2d2UErlCPX4zaaSTblDB8Q75EjqgAjlJMUxy2o288DN2qgptL4qgMbgGmHWeeF/Dv1M7oKGEFTzpB76MbWlDPKDShyW4Ws0pQjw2SjFGAJBEQ4rVJTTiguzl/5HdfKOb5K/u04w1ljRW73wCtAx+LGrXfpecCFXwamLWncOomIiIiIiIiIiGhCYCiHiMbMpy9eaQZzbnlws+dy+cLIWm3cZIx15ka47t4kQzlEk0aggU05elRccM6njPFVzqYcfWTbh0GslQAAIABJREFUuuJnwLmfq38f673oLi/kJw+K25GO2El0AiiJEEd22D2soVLHV5lNOQn762pTzoa7gZ2PizBIMV8+8kV+585Qjgy9hOJKKGcO0DKn+viqxHTrPWrIIhQDrvqlFeRorxTKkeOrZEONbGCq0pSTz4jvV4YRMkPKOlzOtVgHkOrz3g/JbXyVNG2JPegCiO+zaaZ1P2+NoBSfwy2UYwSJel41ttGpLG+0TuUzYh9kq46vphzHuR5tFaGyYlGEcjqXWq/JkIif0VUA0DLXWKePAEmkBUAASPcB3Rvdm5KCYxnKMY5RIWs05YRHvykHEA04B43/TefWlBOOi2UqhfMqKRtfJZtyximUI38jBneLdrNGibYCJ7yr/uNEREREREREREREExZDOUQ0ZgKBAM5aUf1fqw9l8iPe1nce3Ix1rx40H8umnNwIx1gNpke+b0Q0RuRFWz+tF15k00YoJtaTS4uAhzOEIC8U+xmR42b6chFMqYcerT+UI0fK7HtR3I64KccIqAx3e4/0Uanjq2QwRB0J4wyZ7H9J3ObTIlhSaXyVHEkFiHYjwGjKMbbTOhdoniUurlcK5VzyX8DrvyXuy5CFFhHnV/sC4O13Aie/H5h/svfnlOOrzJYb5/iqIff3yTFEgBHs8dGU4zXuy7lewApryO8tPk18D26hHHmuDOwxtqUEeVxDOUZTTu9WcZtQQjm60pSjha3AjgzFOOmVxle1iu8v2Q30bQO6liv7ZfxdeoV9nJacCbzjXvH3WE0wKII5+18WwZyZR5YvI4+vHh39EInZlJOzjqv8/PWOt/OjdZ5orQLcm3KOvhI47u31r988twLi713+7Yz3+CoA6Fw2PvtAREREREREREREkwpDOUQ0pk5cPK3qMs/t7K/4+t+39+Ib9230fD2TL+CLf1yPK25dZz4nQzm7+9Neb/OlL5Ub0fuJaAyZ46tGcEFahgFCMaPdIwWgVN4A0zxLjB5Z8fr6t1WvFZcAR19e33vlReVdT4tbt3BFLWQjSbLbPbzk1LYAQEC0jMg2mnCz9bo6vqpYBA6sF/fzGRG6kWED2fAjgw8BDSgpAwttTTlGEKjFGF+V6rVedwvlNM8UywHWuaSOL2ubL1qOKrUCyfFVctyTGbTx05RjhBAizY6mHJdQTtzv+CpH244M2Ewzmj+irUB6wFo+1SvGFAEixATYm4G8RjeF4t5NObm02A89IlqL3nYXsOoy9/3VqoyvAoCdT4pwTpfSlCP3y+/4qmAQmL/W37Jy29seEfdnrHJZn6PJaTTJY2SOr4qMXVOO5NaUc8K7gOOvqX/98jvUI6JFRp73zvNgrNhCOUu9lyMiIiIiIiIiIiIyMJRDRGMqpAXxprXzKy7zsV89h0Kx5Pn6P/z3I/jKn15BqeS+TPdQ+YipVK7gsqQ/6nb+/NK+utdDRGNMBjTcwgt+mQ04MXtbR8hxkT0QEBefE9WDhw139OXAqdfV996OxSI4sNsI5TRkfBWAof1GU06V9S0+Hfjg82I/qo2v6t9uhWfycnyV8R2HXZpybOOr1KacdhHmSXSJ8VUA0L9L3Aar/E9jZzOPX+GE2N+M0YhjNuUYx8crlKM25URagMxgeduOKtZuhZgqyTtCObIpp/MwcRttcTTl9IlgixaxmnLUcU1egaRIs9KUo7TVhIymnHzW2oeFr/FumtKVsIdbUw4AbPqzuJ11TPn7/I6vqlW0VQTQAGDGyvLXzfNlLEM5GWV8VcS+H6NBhrUA96ackQoroRz1dkI05TRwfBURERERERERERFNWQzlENGYu+kfrBEPP7um/F+k7+lP45O/eb7qepJZ96BN0SXQ05+0Gm68wjxe1IDQT9Ztq+m9RDSOZJNCQ5pyovZgQKVWlMlED4tAjGxXGWmjR7PRKDO0zwjlVDlOgYB1Ud9tfJVsyimVxJggKZ8RI3OcTSTyOw8EPEI5cWDN24DzvygCOHJ/B3Ya76vyP43dmnL8kOEbGZipuylnoLxtRxUzmnKq/XeukBXBpEBAPI67NeX0i/UUcmK7sXbx3Qy6hHK8jkekxQpSqYG1UMwYQZaxt+B4UcM6XqGcjfeK8E3HYmU7xvnnd3xVreS22+Zb91XyfBmTphyX8VVj0pSjNia5NOWMlAw0aRMklCPPv0SX9XdDREREREREREREVAFDOUQ0rtRxVu85bYl5//bHd7guP5zJm/eHlPsqt5adJ7dZ4zwy+WLZ65XkCrWFeIhogpAXT/UGNOWEYvZQzkjHPE0kXcus+yP9XLF2o01ltxHKqSGMEDXGSqntOrEOETDIDjtCOWnRPGOGZGQox6spR46vigFzjxOtRoDV7DPcbX+/Fzkuq56mHMBq/ZHnpAwteTblZJSmnGajKcfRcqOSx0t+Xi+FrP39MlzQqYRyijlxnGVjTqxdfDcDxvgqWxjD43uOGKPIgrr1/QKiUSWXssZXVaMGkJxhDBmG6d8BzFtrBY0AEdA56X3A0guqb6MectszjnR/faKMrwqOYiinVW3KGY1QjhH4KmvKGaf/M1b+RnB0FREREREREREREfnEUA4RjYuXPnse1n/ufASD1sWzj52/rMI7hE/8n9Wg85NH3Vtr8m5NOSmrKSeTqy2Uk1VCPBGdP5tEk4bZlOOjicOLeSHYEcoZi4vsY6XT+O0NJUZ+oTsQAFpmieBGLlnb2B5NB7qW2y92y7BIqselKadQHnqQ+x/UgJLSpqaOr1LJcE3WGCs1ak05xv6ZTTnO8VVD7u8r5KxtylBOISNCFm7fVaxd3CarjLAqZO3tKQtfC5z9b8BhZxvbahG36X6rRSnWLvYh3Scey6acgObdxBI11hOfZg/LuI2vqkQN7gQ8mnIAYP6J9tc0HTjvC+KcHA1y2zNXub9uNuWMQYhPHqNC1gpdBQIi0KSNYqtM03R7eLHRnOOrtPEeX2WcfwzlEBERERERERERkU/j9P+bSUSHunjY+vl55sZzsKc/jYB6wQ7Aczv7cNTcNttzf355v3n/ia3uFx3zRStEUyqVytabyRcA+P9X49mCtb7DpjdVWJKIJpRgI0I58mJz1H7BeUo15SwXt40KDjTPFiOOskO1h5f+eZ09vBEzQjlJI5QTioswRyEjAitmKMfY96pNOY7PKPcv4zOUI7dX65gec3yVEWiRAYNYuzg/B3a5v082ngD2phyvdhkZykn1WmPB3DgbavQIcMqHrMcybJIesEI4silHajeacsIJ+3emkk058U7786EokKthfJXmY3wVAMxzhHJGm9mU4xHKkQ014TH43w7q+Kq8clz16Og25QQCQOtc4OCmUWrKcY6vMrbhDGeNFTblEBERERERERERUY1Y+UBE464tHsaKWeJf0//P2443n7/km38rW1YdWXXhke7/8j2vjJtKG60405sjCGkB23N+5ZRQTipbqLAkEU0oMijQiKacUNweYphSoRzj4nKj2n9kU052uPagjzPcIZtyhruB7leAmUeJx3J8lRmSkeOrjAv1ZaGcKk05mUHrfZVo9Y6vMpZPOppyghrQNh/o3er+vkK2fHxVpSCL2ixUSSFX+e9Cjpoqa8pRwiWJ6eI4VGpHiRihlcQ0+/NmuCrnb3yVuowzlCNbffQoMOvo6utqJL9NOWPxeyG/z3zGflz1iHeTUaO0zRe3o9GUUza+Sv7tjNO/LZHnXxdDOUREREREREREROQPQzlENKGcsXw6bn7DkebjossoKkkdSaUqKO9J5USIJl8soSkiLuCIphz/5PiqkBYw10dEk4AMaPi56O9FlxeEo9Z9YGzG0YyVaYcDCDSuzaNltmh+KWRHvk7ZlLPtYRFGmX2MeJxLi/FU8sK8HhZtILU25dQ6virYgPFVcqyQ1L7QO5STz1ghhEgLkE8B2WT1ppy+7cAtpwBbHnJfzjm+yinqMr4q3m413yAgPlOso3LgxKspR4+KYFXeZ1NOpfFVmg6Em4HZq61jNVaWXwisfQ/QttD9ded4tdEkj2MhZw9uHXsVsPS80d12q9HKNBpNOfLY6Y6mnPEK5UxfKcKBs1ePz/aJiIiIiIiIiIho0mEoh4gmnGPmWyOruocz5v1ndvThyhOscRxbu4dd359XQjnJrGjWyeWLaIqKCzj1NuW0xkJIsimHaPIwx1eNoCXCbMqJTd2mnHBcNF00KjjQPFuEPoCRHyfZ/PLE98XF+BUXi8cyZKMpF+bDces7DwSBYhEYOgD0vOrdlKPpIryQHbbeV0ndTTlGOCnZYzXfSJVCOYWMvSkHAJLd3kEWGWJ65nZg7/PA1vLGOQBGGKZCWE02wGQcTTnyc0RaRLAoXiWUI8M9Cef4qnhtoRx1X93CGEf9P2DNW6uvp9FmHQ1c8O9A0OO80cZjfFXGCF0Zx/XsfwOO+IfR3fYRlwKr3+I9xmwkgpr4/uXnMVumxun/jJ2xEnjPX63fJiIiIiIiIiIiIqIqGMohogmnJWpdQN/bnwYgAjiXfutvuP3xHehsEhdkfv33Xa7vV5tyZJtOrlg01yuDOn5ljVBOSyzE8VVEk4ls1KgUPqhGtjKEYvYWiLFovhhLa94KLH9dY9bVoowWHOlxkmOUMgPA0VeIFh7AarZRAxrhpvKmnD9/GrjtH71DOYAIiJjjq6qECrQGNOU421zaF9obaVT5rNKUY4Ryhg9UaMoxjtf2R8TtgPt/J8X4qkpNOUYoJ90vthfQxCgquQ/ytm0+0DTdez1ytJSzKSdk/C1l+v2121QaXwUAr/uaOD8mGtmsNBbNWjKskjUCa2PZGrTkTOCSb4ze+tXfX3kujFdTDhEREREREREREVGNGMohoglndlsMlx4jLrxe8k3xr/w/9qvnzNe1YOWLpvmi1YTz5FZxkTNfKKEtLi6ODdcayjHGVzVHQ8gWihVHahHRBBJrE+GMRFf96zBHpsSsIAEwtZpyAOC1HwFe8/7GrKt5tnV/pKEcPSxGEwHAie+1LsxnXEI5oXh5KGdgF9CzGUj3iXCWW6AjnFDGV7m8rhrp+Kpi3r0pB3Bvy1HHEMnWmeGD3kGzUMw+Zs0zlJOt3FATUcZXDewWYahgUGnKMb6TS74BXPa9CusxlktMc+xn3Fq/n9Ccuq/VvqOJRAsBJ78fWH7RGGzLOI7ZQfvjqSCcsI9xC+r+GpaIiIiIiIiIiIiIJgCGcohoQvr4RSvM+0d++h48tqXHfKwHg3jDsXMAAAuvvxPv/vGTeHnPgPm62pTT1RxBqVRCvlhCW0xcwBnK1NZ2kyuI9bUY469kcw4RTXAts4F/eQo47Kz612E25USVppxA7aGMQ0kjm3IAoH2BaPHpWmp9B3LcVFBpewknykM5qV5xe2C993cWitUwvsoIAdUaygopx8HZUGOGcraVv6+Qcx9fVakFRY7VaZoB9NcZygnFxLFND4hgj2woijhCOfGO8tFUqqhHU478HtMD/sIV1ZpyJqpAADj3c8DsY0d/W/K8koE1rzalyWj6CqBzqbi/+s3AW/8wtT4fERERERERERERTWns/SaiCWl6s9VIMZixN9sMZ/O20VX3vrQP9760D1tvFv8SPa+EcgbTOTNU0yqbcjK1NeXkjBBOU0T8ZKZzBURDk+iiINGhrGPxyN4vL/yG4kpAJ159zNGhrGmmdb8RjUJv/o01/kd+HzkZylF+i8MJIC9GHtpCOQCw9wXvfQnFgcF91vsqqbcpR9PF+ZNPl4cJ2haIW7emnHymfHxVLlm5BSXWLt638vXAM7e7L1PIVv4MgYAI1MimnJlHiuedTTnVJIzRVq1z7c+b30XJ35iloK4Ervi350qGm+QotkrjySabq39l3Y+2AgtOGr99ISIiIiIiIiIiIqoRQzlENOn0JXMVX88X1FBO3hxn1V5nKEeOr5KhnEyeTTlEhwwZxNGVppzwFBtd1Wh6WIQxhvdbIY6RaFLGj8kwitv4qsVnWGOoghpQKgCpPvF4eD/QscR9/bbxVdWacmQop45zQIaGnIGaaAsQn+YxviqrNOW0WM9XCrIc/05xXFJ9YpRRul8EGZzrdT7nFG0V7+3fBSw939iHZvttNQtOBt5+NzBnjf15NRDkZ8xSICCWK9XWdHdIkaEc2fo0lcZXEREREREREREREU1iHF9FRJPS0586p+y5DXvFvw4vFK3QzFAmbzXlxELmc7WQ46qe39UPANi0f6j2HSaiySmkjK8KKU05VJkcYdXoAJOmAwFNCR4obSCnXQec8xlxXzblZKzRht5NOTEAJet9ldTblANYo7zcAjXtC/035QCVAxfHvQNY/RagVYx5dB1hVchVHxsVbQX6tgH5FNBirKvWppxAQLSaONttQlYbnu9GFz0svntyJ49j1mjK4XgnIiIiIiIiIiIiogmBoRwimrD+35q5nq91JMovJp739YdQLJZs46uG0nnkjVBNRNcQD2sYTNc3viqZFf9Cf3dfqqb3E9Ek1rkM6Foh/p/OUI5vzbPFrQyiNJIetUI5QY/SR7dwjVeQRv0+qzbl6OXv8UsGWtwCNe0LgZ5XgftvAu7/oniuVAIKGaUpRwnC+Bn51GL8N3TAJZSTz1QPw0RagP0vG+uabd8HtbWnHurx8xse0SL2cWVkZ46vMoLDU2l8FREREREREREREdEkxlAOEU1Yn7hohevzYU38dP32va8pe23fYBoFNZSjNOWEtCASER3JbH3jq649bTEAYF6H/WLsvS/uxcXfeNi2XemVfYM1bYuIJpjWOcB714nboC5CGxxfVZ1sygmNRignrIyp8hHKqdZuowaHxrMpp28b8OC/A8/dIZ4rGKMaZdgiFLf2z89oItmU4xbKUcdieYm2WsdZNuVEamzK8aKrTTk+Qzk6QzkVmeOrZCiHTTlEREREREREREREEwFDOUQ0YbXFw7j3Q6fipc+ehw+dvdR8fvWCNgDA0fPayt6Ty5eQN0M4AQym82bTja4FEA9rZuONX/L9HXFxwWvYMf7q3T95Cs/v6kdvMmt7/nfP7sa5X3sIf3ppX03bI6IJKhAA9BibcvxoXyRCAjLE0Ui+mnKUcUkzV4nbiuOr5PuqNeU0IJTjFpboXCZum2cBqV5xv5ARtzLEEwhYYRg/7TJNM8Xn8RxfVaVJJdpq3ZdNOWHZlDPCUI76XfhtdNE4vqqiYFD8PQwfEI/9tCkRERERERERERER0ahjKIeIJrSlM5oRD+v4wNmHY+vNF+HxT5yFn75zrfn6shn2C4ObDgyajTVNER3pXMEcZxXSAth2MInfPrO7pn3IGiGfaU3iIqjX+KsDgxnb45d2DwAANuwdqGl7RDSB6ZHRGck01Rx/DXDNffWFV6rRIz5COUp4Y84aces5vkptygm4LyOZoZwRjK9yC0usegPwT48Aq98KpPuBYkFpylECOHJslOYjcKHpIpjj2pSTqb4OGcoJBIGmGeJ+8wwgMR2Y7t5k51tIacrxO75Kj3p/3yQ0zQB2/13cZ1MOERERERERERER0YTAUA4RTSrTm6PQNeun6873n4IfveMEfOmNRwEA3vmjJ80QTjysI5MvIi+bcoLuP3l3PrcHtz++3XObcnxVR0JcwNzSPWx7feE0cXH2oVcO4OCQFcy5bd02AMDmA/bliWgSC7Epx5dwHJh11OisW23K8WpZURtvzFCOx/emjiOrNh6pEeOr3MISWgiYcQQQ7wBQAlJ9QN7RlAPU1pQDiBFW/TvLny/kqq9DhnKaZ4mAj9z+dRuBw87yt30vtqYcn40uepjjq6q59q/AG74LnHodMPe48d4bIiIiIiIiIiIiIgJDOUQ0yelaEKct7cIRs8XFw1IJ+Pj/PQ8A2NWXwsObupEzx1m5/+S992dP44ZfP2+Gb5zk+KrOJnHh8D/v22h7PWA0K3zxj+vxum88bD4/aIy5WjWnFUQ0RZzwLuDIN473XhzatDCQHRL3vUIatlCOEU7wbMpRAiLVxlfJdYTrGMslt1MpDBNrF7epHmt8Vb1NOYAYOzXg0g5XyPofXyVHVzWSrjTl+P0sWoTjq6pJTAOO+kfgzE/6D24RERERERERERER0ahiKIeIpoR42PtCnQzVhLTKY0ne97On3d9vhHWaIqIp4KKjZtle14PWevf0p837Zy2fDgCoslkimkxO+RCw/KLx3otDm9qU4zm+KmjdTjsMaJ0PtC9wX7aWUM6yC0QTScfi2vYZUJpyKoRQYh3iNtkD5LPivhquqLUpp2WuGF9VKtmfL2Srh2FkAGg0QjlqQMr3+Co25RARERERERERERHR5ONxJYOIaHKZ3+E9TubhTd0AYBt7VSqVzIYb6d6X9rm+P1soIhAAtGAAc9piiDgad2IegaBnd/YDAJK5QvUPQERE/ugRq0Um6NH2IsMb0TYgGATe94R3CCVcQygnnBBNJPWQ7TqVQihxpSlH7pfmMr7KbQSWm9Y5QC4JpHqN0VgACnmgVKweyjGbcub421YttJAIVBXztTXlMJRDRERERERERERERJMMm3KIaEoIBr3raPb0pwAAIWWZIWO01O2Pb6+67myhiJAWRCAQQHNUN8dSSXI8llP3kLhonMoylENE1DBqqKVaU44cBxWKinCOm1qackai3qYct1CO7jPIMn2luN2lNMEVXNbrZjRDOYB13P2GcnSOryIiIiIiIiIiIiKiyYehHCKasr75pmMBAAGIMI6uBXHGsi4AwP5BEZi54dfP297zf3/fWbae7zz4KrLGCKvmqI7BdM72uhyPBQABl2xQkqEcIqLG0aPWfc1nKKeSiRTKkW02qR6rDUgfQVPOvBNEcGnb36zn/IZymmeK23pGdfkhv0e/AaNjrgJOfM/o7AsRERERERERERER0ShhKIeIpozfvPc1tsdHzWkDAPxk3TYAQEgLYOvBJADg63/e6LqOXz5VHspRNUdDZsuOpIZySkppzuxWccExNcLxVQuvvxP/cff6Ea2DiGjK8NWUYyQk/YRyahlfNRJ+xldFWsRnSvYo4ZmI/fVq67BtMwHMPhbY9oj1nLlej9Ff0rQlwLvuB5ae729btQrFjP3w+VmWXwgcf83o7AsRERERERERERER0ShhKIeIpoxj5rVh680XmY8TEfuYi5AWRGtMXITcuG8QxWL52KlVc1px9lcfxAMb9uPWhzYDABZ3JrBilrgQ2hTRMZh2hHLyxbL1AEDGeH4k46tKRsrnvx/YXPc6iIimFK2O8VWVhBLK+7xHIY6Yn6acQEDsc6rXGl+lBnDM8VU+gywAsOBkYNdTQFaEUn035QDAnNXeY79GSoZyavksRERERERERERERESTDEM5RDTlNEfFRdqWmL0FQNcCeP0xswEA6/cO4qjP3Gu+9uynzwUgRlVt2j+Et/3PE7jprvUoFEsI60HMa4+Z63aGcrIFeyinYIR9ZCgnmbUvX4uMR+CHiOiQZWvK8Wh7CRihTF+hnJjyvjEYX1UthBJrF+Orkt3icbTNes0cX+Vz5BMALDgFKOaAXU+KxwWXsM94MJtyqjT2EBERERERERERERFNYgzlENGU8+t/Ohl3vPtEhLQgvveW48zn9WAQl62Zaz5Wx1C1RHWEtPKGhP5UDulcAbGwuMDbFNUx5AzlOIIzcr2ZvGjISY6gKSc9wtFXRERTjh617gc192Vqacqxja/yWF8jyPFV1QI1sQ4xvurgZrE/7Qus1+ppypm/VhyPrX8Tjws5Yz/GOQwTMo673/FVRERERERERERERESTEEM5RDTlHD6jGWsXTwMAnL1yhvl8WAuiOeI+6iQQCCBXKB9ntWn/ELYeTCKkiZ/LlmgI2ULRDMv0JbMYSOexdlGH+Z71ewZQKJbM9Y1kfFU6x6YcIiIbvYbxVfEO99dVtvFVE6ApJ94hxlf1bAba5tvDM2ZTTg1BlmgrMPNI4K9fBm6aC/z9p8Y6amjbGQ0yXDXejT1ERERERERERERERKOIoRwiOmQEAiJ847RqTovne/7xO48CAH751E4A1mgs2YZzzY/EOJAj57Ti2lMXAwD2DqRt7TmNasoplcpDQ0REhxw1xOHV9lJLU85Yja9qmwfMOgaYeVTl5dSmnGlL7K91LRdBnc7Da9v2uZ8HTni3COg89SPx3Hg31HB8FREREREREREREREdAhjKIaJDRkQXP3nP3niu7fnfvfcU3+sIG405PcNZAMBzu/oBAIPpPC49dg4AIKQFkS1YoZzUCEZQpfPWe9maQ0SE2ppyfI2vGqOmnEgzcO2DwKwqoZx4O5DqAXq2AB2L7a+1zgE++DzQsai2bS86FTj/i8CxVwEZ8d+tcQ/DmKEcNuUQERERERERERER0dTFUA4RHTKmt4hRGa3xEDZ+4QK874zD8Ny/nYtgULTnfOScpZ7vPWK2aNOJhjQAwK6+FACgWBTtNcPZPDoSYhTI87v6kS+oTTn5uvdZDeLIdh4iokOaHHsEeIdyguK32lcoJ6hZwZDRDOX4FesA8mkgOwh0LKm+fC1WXGzdH+/xVTKUo4/zfhARERERERERERERjaIJcOWBiGh0bfrCBdjw+fNtz4W0IP71vGVoiVpNAUcoY6zOXjHdtvxt16wFACyYFgcA/Pf9mwAARWOkVDJbQFtcrOvbD2xGriCeD+vBho2vGq4xlNOfymHzgaG6t01ENCGpYRKvUE7n4UDbgvKmGS9h8ds+IUI58Q7rvnN81UjNWAW0LxT3xzuUo8umHIZyiIiIiIiIiIiIiGjqmgBXHoiIRpeuBRHRtarLJcLWxd3vvfV4rJzVgsOnN+E/LjsKbXFx0fDouW0AgLnt4gJuV7NoV1g+s9m2jWxeNNy0x0NIOUI5G/YOmk071aihnFqbco7+zL046ysP1vQeIqIJz09TzqyjgQ8+Zw+4VBKSoZzAyPatEWLKPvsNFfkVCFhtOePdUMPxVURERERERERERER0CPC4kkFEdOhZu3ia7fFdH3ht2TL8QpMeAAAgAElEQVTBYADzO+IoGGOr5rXHkc4V8SFj9FVzVMfs1hgyeRGmiega8sUScoUiQprIQZ739YcAAC9+5jwkIpV/htXxVYPp+sZXFYolaMEJcKGZiKgR1FCOFvJerhahCdSUI0duBTSgbX7j17/m7cCBDY0fjVWrWUcBs1cDGv/PESIiIiIiIiIiIiKauibAlQcioonjtKVduGpt5Yug8bCG3z27GwCQyhVw/MJ2M3DT1RzBhn2DyBhNOdt7kgCAv2/vK1vPY1sOVt0fGe4Bah9fJaVy9Y/PIiKacHSlWSVQvQXNl4k4vqp9QeNCR6ppS4CrfgFEmhq/7lqsugx49/3juw9ERERERERERERERKOM/zSViEjxo3ecUHWZ9XsHAQALr78Ti7sSiIasi8KvHhgGYIVpjpnXhmd29CEeLr9w3BKtfrG13vFVRaPJBwD6Uzk0VWnkISKaNGQoJxAEgg0K0cimnGCDQj4jIcdXjXeTDRERERERERERERERjdgE+OfAREST19buYcRC5Rdxf/bYDgDAyUvESKyBVK5smVyhVPack218VQ2hnHtf2mfeH6pz7BUR0YQkQznBBrbITKTxVbIpp2Px+O4HERERERERERERERGN2AS48kBENLl8581rzPvFEhBTWnCuO28ZAGAoI0I405vFxeMP//xZAECpZAVxUjkRlvn10zvxrfs3YeH1d2Lh9XfatmVryqkpXGNtZyBdHggiIpq09Ki4DTawAWwija/SI8CFXwaOv2a894SIiIiIiIiIiIiIiEZoAlx5ICKaXM47YiYuWz3XfPzo5oO21wDgwVcOAACWzWwBAOwdSAMA8spYqWRWBG4+/PNn8aV7NrhuK6WEcv797vV42/88jnyhiEKxcstOayxs3ndr6SEimrQ04/etkaGcidSUAwAnvAvoWjree0FERERERERERERERCM0Qa48EBFNLm9cY4VydvelzPvzO8SF3WkJ0ZAzrSlse1+/EpCRoRwne5tOAWHd+ql+YMMBHPaJP2LJx++yteg4qa91D2UqfhYioklFNuVoUziUQ0REREREREREREREUwKvPBAR1eHY+W3m/R+/8wTzvgzQ7DKCOlHdGm1VLJbwxJYe8/G6V62GHdVjyjLpbAGxkOa63I2/fcFz/9RQzmd+/5J5//4N+7HPaO0hIpqUdBF6bOz4qoS4ZSiHiIiIiIiIiIiIiIgaqIFXM4iIDh1RJSizZkGH53LBoGjP2d6TxOW3PoontvZWXXeuUDTvP7OzH0WPUVV/3djtuQ517NUph3Wa99/+P08AALbefFHV/fBr3asHsWJmC1rjoYatk4jIkxnKaeBvTqwd0GNAINC4dRIRERERERERERER0SGP/xyYiKhO/3z6Epy+rKviMjNboljSJRoYnIGclqi4oNwcsecj3/z9xwGIZp1nd/RhMJN3Xfee/jTO+soDrq+lcyLYE9aDiIdFgEgN+zRKOlfAFbeuw3Ff+FPD101E5EqOrwq6t4jV5YR3Ae+4u3HrIyIiIiIiIiIiIiIiAkM5RER1++j5y/HDt59Q9vy1py427+taEF+//FjX9//wka0olUpIKq020sLr78S3H9xcdR82HxjGbmNUFgD0J3PoGc6a46u6miIYTItQT8plOyM1kMoBAHIF9zYfIqKGG43xVdFWYPYxjVsfEREREREREREREREROL6KiKjhbrhwBSIhDavntwFAxbFOA+k8Ch7jqb50zwZf2zvjyw/g5c+ej2AwgKM/ey8A4GPnLwcANEd17BtMA4AZ1Gmk7zz0asPXSURUkWzK0Tgyj4iIiIiIiIiIiIiIJjY25RARjYIPn7MUpy+bbj5+12sXuS736d++YN6/4vh5nut74hNne76WyRcxkM7Znhs0Hq/fO4gXdg2I5XKNH1/1/Ye3NHydREQVBXUAgcY25RAREREREREREREREY0ChnKIiMbAJy5aad5//ONn4Q2r5wAAfvPMbgDAm9bOx82XHWV7T2eTGNHynTevQVdzBK87apbn+p/Y2mt7vKV7GFowYD4+OJSxNeWc9ZUHsO7Vg67ren5nP+55ca+fj2WTzTc+9ENEVCYQEG05QW2894SIiIiIiIiIiIiIiKgihnKIiMbIL95zEv713KWY3hJFWyxse+2TF60AAPzxA681n+seygAA4mFx4bmrOeK57nf9+Enb4ye29iKiB3Hi4g4AQDJbQFppytl8YBhv+f7jruu6+JsP49qfPOX3Y5kGHW099fj5kzuwpXt4xOshoilOjwBBjq8iIiIiIiIiIiIiIqKJjaEcIqIxcvzCDrzvzMMBAD/4m33sUywkgjczW6Jl75OhnLectLDi+vMFK3TTPZRBWA/icmMkVq5QRDpfsC2fLYys2UZt3gGARza7N+/49fT2Xnz0l8/h4m88PKL1ENEhQI9wfBUREREREREREREREU14DOUQEY0D2WAjBQJi1FRLrLz5IRERF54XdSbM55qj5RejL791nRnuAYCwFkQiLJYbzhSw1aOBplAsIecS0HF7TuUM5Ty1rddjSX8u+/YjAIChTH5E6yGiQwBDOURERERERERERERENAkwlENENA5++s615n0tGLDdv+68ZbZlZbBGdeScVqxd1IHHP36W+dxT23ptbTiRUBBNRnhnKJNHU8S+nqUzmgAA//idR3H4J/5Yto0ntvZU/AxyHNY7T1kEAFg1p7Xi8tWUSiN6OxEdSvQooDGUQ0REREREREREREREExtDOURE40DXgtj0hQvwg7cdh803XWh77b1nHIY3rJ5jPnaGaQDgY+cvxx3XnoTpLVHccvVq83k12BILaeZ7hzP5snFVQ2nRSOPVcDOYrtxYkzECQIu7RIPPv/7i2YrLV3PtqYsBACcvmTai9RDRIYBNOURERERERERERERENAkwlENENE50LYgzl89wfe3io2eb99VRVe8/8zAAwFFzrVaa81fNcl1HezyMkCZ+5q/58ZPmWKjbrlmLi4+ejd39aZSUFE/JUVWjKw0+bmRTTlssXPba9oNJFIq1Vd8MZ8X+PbL5YE3vo6mrVCqhZzg73rtBE9Ha9wCr3zLee0FERERERERERERERFQRQzlERBPQKYd1AgCuOH4edM36qf7wucuw9eaLEAjYAzOvPbyzbB2PbelBayxkPn5l7yAAYPnMZvz+2d0AgA/e8Yz5+lfufcUWpBlI5yru4+6+FABAC5Y/f+qX7sdl336k4vsB4NaHNuOq760DACSzhSpL06HmR49sxerP/Qlbu4cbts5HNx/EJ3/zfMPWR+Pk2KuBla8f770gIiIiIiIiIiIiIiKqiKEcIqIJKKQF8dJnz8MX33Ckr+V/8s615v3V89vM+13NEfN+T1KEbCIhzXzut8/sNu9/8/5NyCkjrgZSlcdXfeL/RLDh0c0Hcd4RM5AIi/XKMM8zO/qq7vdNd63H3zaJZpyUEsrZtH+w6ntp6vvLhgMAgC0NDOVc+d11+Om67Xh5z0DD1klERERERERERERERETkhqEcIqIJKh7WyxpxKulsEgGc2W0xAMCaBe3m+CoAZjtOVA/ibScvLHv/lSfMt4Vyqo0NWjlbjNB680kLMas1hmEjVLOujvFTyWze1pRz9lcfqnkdtSqVSvjVUztrHrNF48D/n4Fvl3zz4cavlIiIiIiIiIiIiIiIiEjBUA4R0RTx+MfPwi1Xr8FbjcDNkXNay5YJa0HoWhBXrZ1f9trtj29HrmAFVP7zvo0VtxfSRFJiUWcCP3xkKwBgb38a//b7l8qWXXj9nfjqvRs81/Xo5oO2ppyxsOiGu/CRXzyLc7724Jhul2qXyxerL1Sjk5aUj3wjIiIiIiIiIiIiIiIiaiSGcoiIpohgMIDzV83E8Qs7cMvVa3D9BcsBALdcvdpcJms04SzuanJdx1t/8LivbX3pnvX44wt7AQBaMID2eAgA8MTWHttyxWLJHBP0X3/ZZD7/3YdexZu//5i174EAkrk8jpprBYmGM5XHZzXK3v70mGyHaicLcnb0phq+7odeOdDwdRIRERERERERERERERGpGMohIpqCzl81E9GQZtyfVfa6FnSfB7SzN2l7XPQY7XTbY9ttj7/31uMBiPacN66Zaz4/mM5jIJWzLVsolvCFu17GXzd2m891D2WQzBQwryOOG1+3EgAwnB2bUM7c9tiYbIdq1xzVAQDaKIyvIiIiIiIiIiIiIiIiIhptDOUQER0CDp9e3ozz82tPwi1Xr8bWmy/Cf15xDADgoqPsAZ5Bj7YaZ9AmrIn/nHzvr1tsY6he3NOP3qR92b9t6oZTz3AWyWwB8ZCG2x7bBgC487k9eH5nv7lM91AGdzyxvey99VDDRq/sG2rIOqnxdCM8NtzA0WbjGcK64dfP46frto3b9omIiIiIiIiIiIiIiGhsMZRDRHQIuOeDpwKAbTzUCYs6zBad1x01GwBw/3ox0mfZjGYAQL8jUCM5C3QCRpPJk9t6bQ033//rFhwYypiPk9k83Lp3epJZJLN5xMMaPnD2UgDAHU/swMXffBgLr78TAHDc5/+Mj/3qeTzpGJEFAD9dtw3v/vGTrvvqRo7xkvKOxzQxyHMl2cDWpJzxXTdF9Iat06/bH9+OT/7mhTHfLhEREREREREREREREY0PhnKIiA4BwWAAW2++CL973ymur8txVrv6UgCAw2eIZp1Tv3Q/Fl5/Jx579SDyhSIWXn+nGZIBgNuuWQsAmN1mtY8kMwUs7kwAAO5bvx+DaSvYs/LGe/DWHzxetv3e4SwG0nmEtCBixtit9XsHXff1jbc8WvbcJ3/zAu59aR8yeX+NKs5QzrU/ecrX+2hsZfPie0o2sCknVxBRn6FMHpl8Aelcwfd5I+3uS+HDP3+mpvcVPEbBERERERERERERERER0dTFUA4REZXpSIRtjy+/dR1ueXBz2XKvOazTXP7KE+ahLR7CcDaPRUYoB4BtnJWbzqYIfv7kTgDA9x7egtOXdZUtc+tD5duWDgxaTTw/e8zfeKtc3h7KuW/9fl/vo7GVkaGcTANDOcp3/9irPVj+qbux7JN317SOG3/7In799C48sOGA7/dk82xjIiIiIiIiIiIiIiIiOtQwlENERGXesHpu2XNfvveViu+Z2RJDXzKHF3cPYL8SlPnOg69WfF+3Mt7qTWvnI6QFbaEeALjprvW2x6WS1Tpy/Bf+bN7vHc6a9189MISntpWPugKssMdJi6dV3DcaX7KJZthjfNWOniQu/86j6EtmXV93ky0UMbMlCsBqiKrVE8YItVrab3JFhnKIiIiIiIiIiIiIiIgONQzlEBERABGIkZIeIYhKmqO6ef/5Xf3m/WyhCL1C+KGrOWLev+GC5QCAllio4rZ6kznX5//rL5vM+2d+5UFc9u1HsbM3WbacHId0pfKZaeLJ5ESQxatt6eyvPojHtvTglirBL1WuUMTyWc0AgD+9tM/2Wr5QxLaDw1XX0Z/KmevyK1+wAjy1jssiIiIiIiIiIiIiIiKiyYmhHCIiAgB84dJV5v157XE8c+M5iIaC+PZVq12X33zThbbHO3tTtsefvnileT/v0ijyobOX4rrzltnGTzVHRRjn2R19Ffe1eyhja8tRDWXsgaL/fqB89JUMHSXCGo5b0I4TF3dU3B6ND9lolPEY/SSfdxut5qZQLKFYAtqM0NcPH9lqe/29P3sap33pAezpT7m823LpMbMBWOEuP/JKgGfYMY4rmc3b/g6IiIiIiIiIiIiIiIhoamAoh4iIAACBQADnrpyBK46fh3kdcbTFw1j/uQtwwZGzcN9HTjODK1tvvghbb76obPTPNa9dZN7/zCVH4O2vWQQ3Xc0RXHvaYnzg7MPx3jMOw/KZzb73cV5HDABw7tcewqIb7sLLewbKlnlkU7ft8amHd5UtI0MR8bCOaU1h9A5bzSdbuqs3pdDYkGEur2YZGY45dWn5d+xGNtvMbouVvfa/j2/H5gPiu5fngxcZHvum0sxUddtKMG0obQ+OrbzxHtsYNiIiIiIiIiIiIiIiIpoaGMohIiLTrW85DjdfdlTZ80u6mnDbNSdiw+fP93yvGnR4y0kLAAAfPX9Z2XKP3XAWbrhghfn4jmtPAgD87F1rzecuOnKW6zY+co59fRf851/N+7e/60QAQNpoT5Ghob5ktmw9siknHtawszeFDfsGMZTJ49Jv/Q1nfPkBdA+xtWQikG1I6Zx7U05nkxh9dszcVl/ryxqhnI5EuOy125/YgU37hwAAdzyxveJ6ZB5tV1/lRh1VTmn7cbY5ERERERERERERERER0dSkj/cOEBHR5KAFA9CCWsVlbn3zGuSLJQQCIrXwz6cfhjedMB+6FkQ8pCFfLCHoaNhpjYWw9eaLbM99803HYs4fY7hg1Uy8uHsAn/zNCwCApTO8W3XmT4sDAFJK4GYwnUePI5STzhXMsUOJiIYXd4u2nVWfvsdcJpkpAE0VPyqNgUKVphzZfON3jJQMxoS08kyyOjLtR49uw2dev6psGSlbcB+dVkm+WD2UUypZfztEREREREREREREREQ0+bEph4iIGubcI2biQkfLTVs8jKaIjmAwgLDu7z87gUAAH79wBY6d346rT1xgPi+DN25aY2KkUH9KjB4KGuGG/7h7AwAgmy9i4fV3Yvmn7sY9L+4FIMZXfePKY8vW9dFfPYuUz6DHaBhI5/DYqwfHbfsTRaFKU45svknm3L+rQrGEvf1p83HOCNOEtCA++/ojPLc7qzVacb9kGKgWOSXIM+wRykl5fA4iIiIiIiIiIiIiIiKanBjKISKiCe/37zsF1562GImwd1OPfO1rf9oIoLxd5dJv/c28/4fn9gAQbToXHz27bF3rXu3BihvvHvF+1+uK76zD5beuw0A6N277MBEUqzTlZIzmm7RHgOq/7tuIE794H/b0izFTMkwT0gI4fLp369Lymc3IF4q44dfPYUdPsuz1rDKKSo7YqiavhHIGPUI5HGtFREREREREREREREQ0tTCUQ/T/2bvPuCjOtQ3g1+6yLL0LVkSs2EVj711IomkniUlMM/GkVxPsxkp6YmKqKSdGTdPEN6JiV2yoYEdAhFVEadJZYOv7YdnZmS2AXZPr/2nmmWeeeWZ38/7Uc733TUS3vC7NfTFtXISktc/M6AjJHMu1Kp0BX+08I6mu8tuhbKRcLLNb18O17i6Oy/epsSb5/FXs/MpY9nowq+iGP/tWYqmUU6N3UilHX3f7qt0ZhQCAs5fMwRpLZR1XFzm83azfvatNO6tKrQFHz5di1YFsvPrrEbt1/+/oBeH4fHFVg97lix0ZwvEfSY5/U8fPlzZoLXLuj6TzSM8rv9nbICIiIiIiIiIiIiIiAgDU/b9GEhER3aJ6hPpBHRvt8FrshlTJ+Vt/HLObo1TYt9Pq1NQHJy+YAzFRXRpj1tqTAIB7I5tfiy1fNkuY5N/KWJvFqXbS1skSynHW9smjtnqSpRWZXtS+yktl/SOQ1qYdlUarR16Zue1V0tniOvd4qVKLQe9tBwBkLBwHF4XjvPOGE7nC8a70AuFYXGnHU8U/ll2tN38/CgBO/28DERERERERERERERHRjcRKOUREdFvZOXUopkd1QM+WAVe1jrvS2gqrWws/ABACOYC0+kpDWxRdK0FeKgDAvHUpN/S5txqD0Vopx9F3YGlHVeWkUo5Cbq6eZPkure2r5MI1RzQ1Bjy/Itnp9V4t/YXAj7iaUZFGC8D8e9E6qe5j668jOcJxWdW/u13Z1brR/50SERERERERERERERHVh6EcIiK6rbQM9MSzg1s7vBbo6So5Dw/ydLqOuCrJ8PbBwnGQlyt83ZUo1lgDEptT8i5rj1tP5eHspcrLukese21I6N/O0r7KZAIqavR21/W1oZ0DasdtvixtqSpqzN+lVgjlyBDso3L63MxC6XdnG/ao0RvR2McNAHBQ9OxLFeZQznvxaWg3cwP2nbkEAEjMvOT0WfvPWO8vZSjnqugMDOUQEREREREREREREdGthaEcIiL6x5jUL0xyPj0qQnL+89N9MKx9IwDW1kYAcHf3psLx6E6NMbhdI2TklQtjP+07a/csrZPqLQDw9P8OYcj7Oy53+wKdwXmVlRM5pRjz8S7U6B1Xh/knMRpNUNW2GCuutA+s1PU5AYCqthpSSW3ASldbvcZVIYfKxfr9928daHdv60bWQFea6LcAADV6A8JqA1+bRIGt/bXhmy93nAEAPPztfgDAg9/sd7pHS3UdAFiWkFXn+1Ddqv8F/00QEREREREREREREdHthaEcIiL6x3h5RBukLRiLobXBGwCYP74TAOCnp3pjYNsgJJ8rAQCcKbBWQ/H3UArHbi4KlGi0qBS1RErLK8eOtHyM+zQBgDmQ027mBkTM3giNVlrBpUQUsrhSdbU+uvOz3UjLK8emk5dXved2ZDCZhFZeRQ4+V72oMorRaB+QsgR6Fm9IBWCtpKKsHf/vkNZ4c3Q7fPf4HVj+dG/JvZbnAsDsv05KrtXojfB2c4GtjzenA4BQRQcAXvv1iGSOpW1atU7aUguwD/9o9UaExcThlwPn7J5F9mp0DWsZRkREREREREREREREdKMwlENERP8YMpkMKhcFnhtibm/VtYUvHusXhpPvjMHgduagjqMWQT5uolCOUo6E04WS6wXlNXjih4M4dbEMYTFxyMivAABU64zoODseALB8nxphMXHoPm+zcN/qpPMN2veBLGn7JXEVHEdhE/O71r1mldYgBD9uV0ajCXqjOWgxYekeu+s60WdT5eBdjbWVjJr6mkMyOqF9lfmPPzHjOuDF4W3h7qpAWKC01VmxRotWtdVwDqiLsCLRWi2pSmsQAj9iZdXmgFa7xt7C2J+Hc4TjhLeG4Y3R7cxza3+HWaJWWQ/3DpWsl1dWbd7nmuN2z7pZ9p4pxKmLZTd7Gw79G6pHERERERERERERERHR7YWhHCIi+sfpEx4IdWw0gr3NYQxPlbWqyV3dmtrNl8utCReFXIYHe7Woc/2oJQl2Y7PWnrQbe+P3o8jIr0D8yVyna93/5V785+t9eHFlMqatOYZzlzRCNR8ASLQJ7FjI4DyVYzSaEDF7IzrM2ljXawhiVh9DWEwc0m0qtdxsRhPQLsTb6XWdqKJQZY3e7rqlkk63Fn4AAK0QyrH/7Pw9XSXn6XkVCA+yBnVm/HlCOK7WGeCuVOCbx3o63Neu9AKH4y0CPHDygjnQ0nvRVgDA2Usa4Xpibfsri/rac10v9325F78fynZ4beK3iULFqFtNNSvlEBERERERERERERHRLYahHCIi+ldZMKEzADhsPwSYQxKvjmqLoe0b4fF+LRu05p6MQqfXRn60E1OWJwEAMvIrkJZrDr4UV2qRU1KFQ2eLAQAnckqx6kA2Br+/XXL/vsxL2HjiIpLOFiEsJk4Yf2FlstNnOqoaI/b97ixM/t9B4fyXg+YAhrMAkIVWb8RXO88ILbue/vEgwmLisPu08/e/GgajCR0aOw/lWKroAECFo1BO7fXy2go2lpCLq8L+jz+ergq7sUuVjluRaQ1GqJQKRDTxkYxb2qYNahvkdM8GUXUfk8mErs190bW5LwAgU1Q1BwA0Wun3uPt0Ibq9swlrj+QgLCYOFTX6ax7cqdIakHS2GFP/OHZN170RxG3f6moBR0REREREREREREREdKMwlENERP8qvu5KzIiKwOrn+kvG54/vBAAwAWji644fn+yNd8Z3btCajyxLdDgeGeonHJdotBj50U6M+WQXAKDH/M0YELtNuC5uUuXnYW2npS6sxH9/TsZ9X+5r0F4AoKzavkWX2Lx1KdhyKl84t1QGmvXXCWe3AAA2peQidkMqJn13AACwNdW8xqPfOX7/+qTmWtqBOa7QYzCZIJfLIJMBvVsF2F2v0hngUxuusgRvxLR686e6v7YCjSXA4uIglCOr7QfW3N9dGDuSXYIXhrWWzDOZTKjRG6FykSPEx01ybUeauUJOscZxmAcAhnVoJBxn5FfAZAICPV0RYFOpB5BW/zmdV45Hv0tEaZUOr/xyBADQeU482s7Y4PRZ9dHqjTCZpO3RzhZVOpkt5ayt2s1kFL1LeT3/DRAREREREREREREREd0IDOUQEdG/zjODw+3aIjXyVgEwVwoRswRkgrxUWPlMH8k1S2UUC0sVHgDo2dJf0oZqdXKOcLzqwDm7PYnbGFXW6NE7zBxC+b+jF5y+R1m1DqUa+/BBgqhyjW3oQiy3tBoAoFI27I8Dlsotluo+V+vbXVkAgE0peQ6vG40mKGQy4bOwVaU1oJm/BwDgXJHG7rqlUo4l8GKpnuLq4vh91bHR2DV1mGTs9VHtheO03HLojSaYTOZqO47WyS+rxomcMrtxS+hrQvdmwtioj3ehSmeAm1KBHrUttsSVb8pEQaNRH+9yuOcrVV6tQ7uZG7B0e4bTOdU2FZeke7v1Qi/iUE6Zg5AWERERERERERERERHRjcZQDhEREYDerQLh6iLHM4NaScaTZo7C3y8OxKGZI9G/dRDGd28KAPBwVeC9+7tK5vYK84c6Nhrq2Ggk2QRX5q9LEY6nrTle5150BhP6tQ6sd89d525Ct3mb7MbjT+QKx+IwkK2+i7cCAGp01rCFuAKKzmBEWEwcliVkAgDesmlpVFebpvpotHqsTj4PACgsd1xZxmAyQSGXIcDTFcUOWklV1hiE0NFLqw7bXdcbzNcs7byEUI6DSjkWcrlMOI7q0hgKuUwI3yzZeho1tWvYBplGRgQDALKLq+zWvLdHMzzUOxSAuSLPqmf6CtfKqnTwcVMKVYfElW+O55Q63aeYbXimISyVhX7eLw2IiVtmZdW200rPK4fJZIKmxnqt2EEY7GYTtwYrq7r19kdERERERERERERERP8+DOUQERHBXE0lfcE49AmXhmEUchm6NPcVzj99qAcS3hqGY/y4TuQAACAASURBVHNGI9jbDXPv6ihca+rnjivx8vA2dmPetW2ZGjL+1c4zWJ10Xjgf07mxcPzboew6n11YUSOpepJ1qVIIN2yprWCzIO4UAHMQSSyzwBzacNR6qT5z1p4Ujr/fk2V33WQyV6SRy2TQG004nV8BALj/y7345cA5VOsMqNIZMKS2WtE9PZrZrWGp7FJerYfeYBQCNXWFcsQKK8xBoC8mRgIA4o5fdBrs6Vv7u7nvy73C2L2RzbDl9cH46MHuUIrmhwZ6CMf55TX41eY7sgSjfJz8BmxptAYYjSYsWJeCtFxzK7BVB87hzs8SAJgrL2m00soxB7KKAAC5ZdWS8dSL1lZiRpMJB9VFGP3xLqw6kI0K0Rp1tei6WcQdtU5caFigiYiIiIiIiIiIiIiI6HpiKIeIiOgytQjwgEttyOKJAa0QFuiB5U/3ho+bUpjjKGjjzOhOje3GjmSXSNphWex+a7jdeOyGVLzx+1HhvEZUOaVnS3+7NcRhj8yCShRW1AjnIz7cidbT1yMsJg7PrUiW3CeuomIwmpBTYq4KU1SpRWXN5bUL+l0UInLEEgxSyGXYXBsO2pKSh0NnixGz5jg6zNoIwNzCys9D6TCsJG63dK5IA11t5Rxn7atsfT6xBwBgRG0VnEf6hCJy/mYAgIer+XmHZ43C8bmj7UIq8a8Oxkf/6Y42wdI2aQDQzM8dTX3d7OZbbEoxVzqyhIjqU1mjx4XSKizbnYUxn5jbXE1bcxwncsqgNxjRaU48Os6OF+aHT4vDq78ecbjW9D+tVZw+35aB1NqQz/GcEsl3nJhZ1KC9XSsnL5TWWxFI3L5qxp8nrveWiIiIiIiIiIiIiIiI6sVQDhER0VXaMXUYBrVtJBm7q1vTBt37YK8W6NzMF8fnjsa88Z2E8Yz8CjzatyW+e7wXEt4aJoz7uLvg0b4t61zTEp7xdFXYtX0qr9ahrNoartDqjVfUisg2hGKpvHKtGEzWUM5TA8wtxSb/dMhu3paUPJRodPhp31m7a3pR6ZThH+60VrmpJ5RjaUEW7G0OzshkMjT1dUPKxTJhjs5oXsvf0xXebkq0DPCUrNG6kfTc1qT+YcLxj0/egfaNvdEuxAsA8N+fzWGoKq19CMXSPk0st6waZVXW71T8XRxQ238v4ooyddlwIhfZRZraM5kklFNfpRydwYjJ/zuIY+dLGvSsvRmFDluUAcClihpEL9ktBLEcOZpdgge+2iecP9irRYOeS0REREREREREREREdD0xlENERHQd+Lhbq+Z89J9udtfHd28KdWw03r2/KwDA202JR/pYwzY/T+4DABgREYIWAR744pFITOwTCplM5vSZYTFxAICq2ooilVoDfjmYjb0ZhcKc9ccvSu559LtElGi0eLh3aJ3vc7G0CiE+KuE84XQB3JRyNKtt2dXQ6jPOrDt2QXJem3mBXCZD71YBTu+rK/ykM5gQ7G3ds9ZggEIug0Lu/DN05kJpNQ6fswZMjmZLwyb3RErbZ7nU0yLLV/T7aN3IHMZ5dnBrYcxkMkGjNdjtNTLUvvLRA1/tQ9SSBOH8P19bwynni6okc2v00qBPI9Hnk1sqbWUFAN/sygQAyGVAZY313vpaa3WduwlbTuXj7s/3ICwmDn/UURmpSmvAxGWJePLHgw6vi5/rzPileyTnLorL/46JiIiIiIiIiIiIiIiuNYZyiIiIroMQH2t7oq7N/YTjhLeGYVj7RpgZ3dHuHoVcJlRpCfJSSa5FdWmCRfd0Ec4trZVs5wHAz/vPSc4nLksUjn3dXQEAU8e0F8YKK7TYkZZf5/sknS1GWZUenZr6AACUCjmqdUahhdWU5UnC3PzyaodVXhwZW9u6S+WikIxbWhHJZUCVznFrLA9XBWLGdRDOq3UGzPrrBJYlZGJvRiH0BqOkfZdWb4TyGoU1/jukteRcKQrhRDTxqfd+g6hcjUppvndAm0BhrNW09ais0SPA0xXq2Gi41IZzwht54pvHeuLonNE4NHNkvc95a/Ux4Xjit/tFlW/s91FSZa5U0zLQw26dFYnn8Oh31t+RuNqSI1U2rabeFLVXs7VwfQoAc8u2pxwEc7SGhv2WxOqr5ENERERERERERERERHQjMJRDRER0nfz6bF88dEcLtG7kiY2vDsK+acPRIsADPzzZW1Kh5Erc2dVcaefQzJGYL2p7FRYTh8KKGgDAwDZBkvENxy/ip31qAMDdNhVmLoqqpLQKsrZeslRqSUgvRJXOgMDaENCLKw9L7q+o0SMsJg65pdXovXArImY7bzVk0b91IBbdaw4aPfPTIVSLghzi9lUD21hbg829yxpmahfiDZlMhvDa/XaYtRHL95/FgrhTmLgsEXqjCS4KOXzcXOCmlOPHvWpU64z17qshmtZWCBJLnD4CADC8QyO7a7bE7+qmNAeSxNVzAODXQ9lwr702LSpCeO7oTo3h665EoKercL0h9p65hJEf7ZKMiUM5xZXmNmaLReEvZ84Xa+qdYyuvzL4SDyANkW1Lzcf2VGlATPyd7UwvcLhGuE27MMu7/NtV6wx21ZGIiIiIiIiIiIiIiOjGYSiHiIjoOukTHojY+7pCJpOhQ2MfNPG1D3JcCw/0auFw3NICy+K5FcnYe+YSAMDPQxoA+XZSL+H404e6C8cbXhkEwBwQAYA8By2OxPou3mo3lnKhDD/tU0OrN2LtkRwAgLtSgU5NfRDg6SrM6zBroxASMRotlXJkaOStwrqXBuLbSb3wxIBWwvwgL/O9b4uq5YhlFVbCaDKhrFqPap0ROoPJ4byGeLyftbXYisl9hCCNWIiPG47OHo3XR7W3u2ZL3KpMVdv6y8PVBYPbSQM9+eXmz/vJ/mHYN2240OoKAGQyGbzqaSNVH3Eo50Jt1SMfdyVOLxxX533rj+di7ZEcDH1/O1JzyyTXnIVA+iwy/zbyy6vx52FrOyvb6kUFtaEyC3HVnSVbTztcu6OoOpGvuxJ55XX/Tv8tus/bhBEf7rzZ2yAiIiIiIiIiIiIi+tdiKIeIiOg256ZU4N376q9uIualcpFUFxnVMQTLJvWCq4sc7Rt7C+N+NtVbhraXhkZ86giFaLR6vPLLYUQtScDstSfRbuYGvPLLEfy4JwtVOgPcXc33zp/QWbjHEvCwhEUslXo6N/PFqI4hAIC0BWPxzKBWQpCoWue8EkjcsYuQX4OOVdOjI7BsUi+oY6MxQFSByJavh1LYc13cXRVoH2L+nF1Fra8+ebC7ZJ6lSoxcLnMY6lLInD9LHPCZMiTc4ZyKGj1MtVWJLC2fgrxUUCrkeHaw43ssXvnlCNSXNBj7SYIwptUb0X6m8ypJJpMJL686jNd+PYqUC+bv+r7I5pI5ttV/xK3Q0vPKHa4rrqZTWqVDZkEleszbhFKNDhn5Fdh9urDOd/mnqtYZcb646mZvg4iIiIiIiIiIiIjoX4uhHCIion+ACT2aSc77hQcCABY5aUUkk8mw6dXBmHNXR6H91ciOIUhfMA4qFwU6NPZG9xZ+CPZxk9z3+uh26NzMWpWkXYg3fnzyDofP6Dg7HmuPXLAbn/t3CgDATWn+Y8hjfa1VYwrKzVVSLO2r5A4CLioXBWZEd4SsNpAS3aWJcO2OMH808ZXuuXerAIf7uxwqFwVG1oaCrpUNrwxC2oKxwnsA9iGo+uQ6aQn15SOReGFoa+F82rgIp2t8sysT5dU6JNQGV0J8zC3KpkdFIHnWKByYPgJjOtX/7iaTCe1mbhDO37+/q92c7/eosT+zCAAQtSQBYTFx+OVgtmTOS6ukrdHElXLKq/UOny2uztMiwBxeKtbo0G3eJoz8aCce/S6x3v3XZdWBcwiLicO+2kpTtxtxRSQiIiIiIiIiIiIiIrpxGMohIiL6B1C5WKuLrHtpIFbUtq6a2CcUR+eMdniPi0KOJwe0wmP9wuyu/f3SQPz+334AgJW1ayXNHAmViwLLn7K2xQr0csXQ9sHIXBR12Xt2E+159XPmZ1XUmEMXxtrCJ3VVghG/h8XKZ/riv0OsYZQFEzrj5RFtL3tvN4JcLpN8b5axn5/ug4fucNySzJnIUD+oY6NxZlEU1r00EOO6NEFGQYVkzqw7OwIAYu+VBrUWb0hFl7mbsDO9AAAkIaEAT1cE+7jhgZ717+ehb/ZLzpv4umP328MkY/PXpTToffQGI7ak5CEsJg5TlidJrg3/YAfO2LxbjahSTnaR48ow4oo7l2vamuMAgIe/3V/PTOd0BiN2pOVf8f1Xo9CmJRgREREREREREREREd0YDOUQERH9QygV1lZP4gozvjbVVzo19UF9lAo5lLVhl/5tgqCOjUagl7mCir+nK354wlwdx1KJRy6XYcnDPRDRxAd/vTCgQftVKa1/DGnh7wEAWLDuFABrpRxFA/+ksu6lgfjpqd5QKuR4oJe1HZKfhxLhQV4NW+QWMbBtECb2CW3Q3Nm1QZuvHzO38lLIZejczBcAEBnqL5n79MBWUMdG46HeoVgwoTOCvFwbvKceoX7C8ern+ttdNxpNSMwqEs7bh3hjQJtANPf3wIEZI+pd/44wf5x8Z4xwXlqlw+SfDknmWNpcZRZWYsSHOyXXqkWVctqFOP6+c0rqb+NUWaNHWEwcdqUXICwmDgtqQ0Tillq5pY6rE9Xn0y2n8cQPB5GYeWOq7VjakgFAdpHmhjyTiIiIiIiIiIiIiIikGMohIiL6hzg4YySSZo50eE0dGw11bDSyFpsrqVytYR2CJUEdALi7W1NseGUQurfwq+NOq4x8a7WTAE9zQCS3rBrjl+5BqUYHAJA3oFIOYA4iDW7XCADg4eoijF8sqYaXm/Vc3OrqVta1uR+mjetgV2nG1lO1QZtG3iq7axFNfJA8axQyFo6zu/Zo35Y4NHNUg/cj/p4dfb+jPpaGZOJfGyxU3An2dqu3WpGXygWeKhfMjDa32VqdfN5uTv/WgZLzEo1WOD52vlQ4fve+rjg21746VH55NQa+uw0vrEyG0WjCBZuQTlhMHDrNiQcATPr+AABg2e4sAEBEE29h3u6MwjrfxVZlbfWnvWfM91nCQSaTCZ9vO92gsNCVEHesctbmjIiIiIiIiIiIiIiIri+GcoiIiP4h/DxcJeEJR2QymaQ90fXy3NDWknMXuQzq2GgkTrdWTQnxcbNeF5XEOZpdgqglCQDMlV+uRocm3vBSuWBUxxBEd22CpY9EXtV6N9KUIa3RvLaC0JUK8HSVfLa2Pp/YQ3L+1tj2TucmzxqFQzNHOvxOzhRUCsfq2Gi766+Paic5H9e5seTcU2UOTrUJNle5SThtH3xp1chTct593mbkl0vDJism90GPUH/4uCnh4SptDZZTXIXzxVWIO3YR4dPXo3+sOaDTEC0CrN+Dy2X8Jjccv4hOc+Jx+Fwxks+VAABe/+0oCitqEH8yDx9sSsew93c0eL3LYRClcsqr9dflGUREREREREREREREVDeGcoiIiOiae2FYG+E42FuFE7WtiUJ83PDycPO1x/uFSe7p1VLabgkAViaeu6p93BEWAAD4dlIvLJ14+wRybpRGohDXjKgIPD+0jdO5AZ6uCKon9NUQs+7siC8f7SkJ7/h7mCslhQWagzeWUE4TX2twq4eDCj2z/johHD/aNxQD2gQJ5ynzxkrmTv3jmN39cccuApAGWGz9ffSC5Pqrvx6BRmsNuRxSF2HdsQsO7313YyoA4J4v9qJPqwBhPOlsMban5gMAtAYj1IWVDu+/GkZR+6ppa45f8/Xp+jt1sUzShoyIiIiIiIiIiIiIbj8M5RAREdE156Wytow6MGMk3JTWqiWvj24PdWw03G0qmax8pq/dOu8/0O2Knr8nZjg+fKCb5Llkr1MzX+H4iQFhl33/6I4hkvNTNkEYsZ61oaunB7ayu5ZdrAEABNm04fp2Ui/hWCYzV1sSV92JP5mHlAtlAICf9195gCsx85LTa6uTz9tVmuk4O144vv+rfXhx5WGExcSh69x4IaCjNxihvqQR5rm6WP/YrXKR49dD2cL50A92YI2Dll1Xw8gwx20tLCYO4z5NwJc7z9zsrRARERERERERERHRVWAoh4iIiK4LdWy0w1ZGzri6yJHw1jCE+KjQxNcNqfPHolWQZ/03OtDMzx339Wx+Rff+m3ipXITvSVlHmytbfz7fHwDw+uh2aB/iDQDo3zrQLmgltvq5/na/h0FtzZVtdqQVCPsRa+GgfVdjUdszAPgjyRxmeaJ/mN3cnVOHYtE9Xep6FeSXV2PiskSn13ekFaBKZ0BvUaUbZ8qq9UJAp82MDU7nlWh0dmOv/3a03vUvh7i6j+tlfLeOsFrLzfP30Ys3ewtEREREREREREREdBUYyiEiIqJbRosADyROH4l900awys0trEeoP9Sx0ejQ2AfrXh6Id+/rgu+fuOOy17EEaX551lolaeUzfYRjH3cXLLynM5Y/3VsYC/aRVtNxU5r/ODt5kH0FnpaBnpjYJ7TOPfReuLXefdboDHBXKvBoX+taYTFx9d4nVlSpFQJMxRrtZd17JYxG67HWYHQ+sR4HsorQatp6JJwuuAa7ostl+X0TERERERERERER0e2J/8pLRERERFdMqZDjwTtCryhENSIiBGkLxqJveKAw1r91kHAsk8nwSJ+WGNS2kTAWYlMp54sd5vY+AZ6u9T7v7bEdhOPhHYKdzkudPxZZi6OE86raUM6CCV3wgail2tM/Hqz3mZZ2WycvlCHYRwWZDCjW6BDk5Xi/xZVadHtnEw6qi+pduy4Gm+o2ZdX21XlslWp0CIuJw8YT1uosO9PzAQA/7FFf1X5uRz/uyUJYTBySzxXf8GcPa2/+zQ8W/faJiIiIiIiIiIiI6PbDUA4RERER3TQqF/swz4cPdMPq5/o7nN/U193huHsdoaDf/9sPAHBn1yYAgOb+7vju8V6SOanzxwIA/n5xINyUCshkMuFael6FULFE5WL94/PW1HynzwSArs19ESZqweauVMBkApZsPQ1PlQvu7tZUMv9odgm2puajtEqHz7dlOF034XQBwmLicCS7xOkcvU11nFIHLbNsdZu3CQDw35+ThbFGXubKRNvqedd/og83pwMAXl51+IY/21LdqCFhqtvFrvQCdJjlvK0bERERERERERER0T8RQzlEREREdEu5r2dz9Gzp7/Car4cSq5/rh2NzR0vGxSEaW3eEBUAdG40WAR5IWzAWCW8Ng0wmw4eiqjduSgXUsdHo0txXGPNSuQjH7q7m0M/IiBCnz/EWzQeAqWPao0cLP8kzAKBdiBdKNDr4uiuFMBAAjF+6BzV6AwBgZ3oBPohPc/icx747AACYsHSP0738sFctOf/R5ryh5v6dIhybbKrvAMCFkipJAEhvMOLUxbIretatxhLAOl9cdcOfXa0zf6ZFlde/1dmNMun7A6jWGbEtNe9mb4WIiIiIiIiIiIjohmEoh4iIiIhuKz1bBsDHTYmuogBNQ6lcrFVwxnRuXOfc/z3VW3IfYA7nHJ41yuH8I3NGY8vrQxAZ6gc3pRx9wwPh56EUrh/PKUXvsACoXBQordLBRSGDm1KBA9NHCHO2pFgDC59vd14txyItt9zh+J6MQgBAMz9zZaHvdmfVuY7OprJOsYMwyGGbyjzPr0hC/9htmLgsURh7a/UxjPs0AcfOl8BgtA/xOGIwmnD4XLHD0M/N5O2mrH/SdZJ01twyq6C85qbt4XpxVVx+qzsiIiIiIiIiIiKi2xVDOURERER0W1oxuQ8A4N7IZld0v5fKBUfnjEbagrEOr4ur9Ww8kSsc+3u6YuqY9gCAh3u3gDo2GurYaCjkMrQJ9sKa5wcgdf44KBVySbAjq7ASjXxUOJ5TCgBIrg1eBPu4CXO2pxVI9qDR6nHukgbxJ63Pjwy1Vt8Z88kuhMXEobJGL7mvtMrc9ujVkW2FsbCYOITFxGH5PjXCYuKwM70AyeeKsXj9Kcz884Tk/rjjF+1CMvd+sRdGUdBm/XHzng5kFWHD8Yt4adVhrEnOAQDc/fketJ6+Hg0x5/9O4J4v9uK1X480aP7Vij+Zi0+3nK53Xptgrxuwm7rtPXPpZm/hmgn2NrdC+ye15CIiIiIiIiIiIiKqj0v9U4iIiIiIbj3ebkqoY6Ovag1f97qrocy+syPmrUuBv6erZPz5oa3Rv3UguovaU9VnycM9cFRUbWbe+M7CcXN/d4dtkooqtRj8/nbhfHC7Rsh3UD2l05x4NPV1Q1m1HhU1ekwZHI6vd2XivsjmmPrHMcncWWtPAgAe//6A072eKahAq2n2oZrvdmfhmcHhduPPrUh2uI7JZMJHm9PRyFuFjzeno1ijQ+aiKOSWVaNpbRWfn/efAwD8deQCPnmoh90aRqMJMWuOoU2wF77ccQaHZ4+2m3P8fClkMqBzs7qrJ2n1RkxZngQAeHF4GyjkztueidtyFVdqkZ5Xjge/2Y9904ajia+7cO1ETinCG3nCw/X6/NXq3CUNQgM9rsvaN5KfhxL55TV4Pz4NUV2a3OztEBEREREREREREd0QrJRDREREROTEXd2aAgD6hQdKxmUyGXqE+gutsBpCU6PHysRzwnmwj0o4tg3kfPlIJABg08k8yfiu9AKcL67CM4Na2a1/obQaFbUVc77elQkAkNcROnEk/tXBAIAf9qiFsTu7WgMUC9efuqz1Wk1bj8+2ZWD22pMo1pgrpIRPX4/+sdtwpqBC0q4LAP46nCNU9LFU6nlp1WH8dug8Fq1PRbFGJ7yj2F2f78adn+2udz9jP90lHE/942idc3UGa1WgHvM343/71ACAfou3CRWDavQG3PnZbnScHV/vs6VrGzH9z+PIKbEPYgGAt8oa8BnzyS6Hc243lgBcVmHlLdeqjIiIiIiIiIiIiOh6YSiHiIiIiMiJRt4qbH5tMGLGdbjiNR7r2xIA0K6xN0Z1DBHGxdVWxC2pljzcA+XV5uDJvHUpDtf8Pek81LHRmDzQPpxjSx0bDT+PuisCWTiqHLTu2EWcXjhOMqbVG+3mXa4RH+7E5J8OScZeFbWwGvnRTof3dZ7jPABzwSbksjO9APszrS2gMgsqheOyKmsbJZPJZNdWqVIrDf+IQzpLt2cAALadyne6l7r835ELWJl4Do8uS3R4XWsw4v6ezQEAVTrDFT2jPjqDEfd8sQfrj1+8pusu3Z6Bid/utxuXwRoQ23W68Jo+k4iIiIiIiIiIiOhWxVAOEREREVEd2oZ4w9Xlyv/YPH9CZ2QsHIfIUH+8c3cnAMArI9pK5qx5fgAOzBiBaeM64O5uTfFAr+aS6+5KheT87bHmkNCM6IgG7eHI7NFQx0bjzdHt6pzX2NfN4bhSYX3/sJg4tJu5AQAwvntTu7kP9GxuN3YlztQGaOIchEaeX5EkHJ/IKRWOH//+AHQGI/LLq4Xzh77ZD53BCJPJhAd7tRDmbhEFaj7Zchpd526CutAa2tHUGDC2U2PhfLOoqs+Hm9MBAHll1U73f6miRggvLVp/CmExcQCAtUdy8Mbv5io9WYWVyC21X0NnMKKxj/W7MJlMKK3SISwmDo995zjIc7l+O5SNw+dK8PyKZJy8UFr/DQ2gMxjxfnwa9p65hHf+Pim5pjdag1x7MhjKISIiIiIiIiIion8HhnKIiIiIiK4zl9pQi7+nK47OHm0XygGAYG83TBnSGoC5PdYbo6wBmj7hAbgv0hp2ebh3qDAvdf5YJM8ahZ+f7gMA6NbCD3Pu6oj0BdLqNgDw4vC2UMdGY2REsDC28dVBde79+yd6AQAW39vF7tqEHs3sxkZEBAttsCTjHYLtxixGdwzBI31C7cadVeRZfzwXOoMRBqNJ0rbqdH4F2s7YgN4Lt+LdjanCeNsZG9Bq2nr8eijb4Xqfbj0NADiSXSIEbSpq9PBwVTicb5GaWy4ca0SVdQrKa9BzwRYhvPRNbTux3acL8covRyRr9F28FR/EpwnneoMRRhMkQbC3/jiGXgs2AwASThciq7ASRZVaZORX1Lk/i7TcchzIKpKMid/tga/2NWgdncGIDzelodymqpDF2UvWUJO4BRoAGEzmylOA9fMgIiIiIiIiIiIi+qdzudkbICIiIiL6N/FtYCupl0a0hburAgviTuHN0e3RKsgTq5PP281zUyrgplRgYNsgxL08EBGNfSCXyxysaPXJQz3w28FsPNCrObxU5r8SKBXme5JnjcL8dSlYdE8XuIuCGw/3DsW0Nccl6zT1dcfJd8bgzd+PoqhSi8HtGmFs5yYAgKzFUSjR6ODv6QqTyQSZTCZUi7EI8lKhsKIGRZVafDOpF/w8lDiYVYwDanOAxBJqcaTtDOfXAODLHWfqvA4AvRZswRePRArn4vZZALDmcI5wLJcBRmsHKxiMJhhEAx1nxyNrcRRq9EbcsXCLMC4+ftRJlZvPt2fg8+0ZeGFYayGY5eGqwOJ7u2DamuP4PUn6vQ/7YIdwnLU4CjKZ/fddVq1D17mbJGOJ00cgpLYCj1x0j0ZrQFGlFgGergAAo9GE9+LTcG9kM7QL8QZgDhrd9+VenCvSoFijxYIJ1pBWaZUOb/1xFA/1tg9W1egNGP3xLpy9pMHANkEoKK9x+BnYmrB0DwDgrxcGNGg+ERERERERERER0a1IZjKZ6p91g/Tq1ct06NChm70NIiIiIqJbkiXccrPYhmrUsdGXdb/eYESbGRvwy7N9cSS7BHqDER9sSsecuzriyQGthHkfbUrDkm0ZwnljHzfkllVj9p0dMW9dytW9BACZDGjoX4OGtm+EHWkFwvmUIeH4emcm1jzfH9tT8/GZaJ9NfN2g1RtxqVJb77pZi6PQatp6u/HerQJwIKsIi+7pAq3egLl/1/2+wd4qDGwbhD8P5+DzhyNhggl3dm1q910BQJCXKxLeGg53VwV+PXgOb6+Whqws3+f78alYut0caoq9twvuiWyG9jM3OpwLWH8XPUL9uifFKQAAIABJREFUcPhciTAe0cQHpy6WCedD2jXCzvQCu/sz8ivwe1I23h7TQQiUWdZ09BvbmV6AtNwyPDvYHGD6v6MX8O6GVOycOlSoSkVERERERERERER0PclksiSTydSrvnn8F0siIiIiotvEzQzkAMDWN4YIx5cbyAHMbbzUsdHoGx6I/w5pjcmDwvH22A54pE9LybwXhreRnO+fPgLq2Gg8NbAV1LHR8HGTFvx0dZHjvfu7On1uU1834fipAa0wMiKkQfs98c4YzIiKkIxtT80HANz7xV5JIAcALpZWNziQ4+y7tLSZ8lQp0KqRV71r5ZfXYE1yDkwm4IWVyXhx5WFJKy2xwgotImZvhMlkEgI5E7o3Fa5X1OhRXq0TAjkAELPmuF0gBwA+3pyOsJg4SfhHHMgBIAnkAOYwjUtt6Ka0ytoCa+RHO/H1zkxkFprbcf0hqgw0bc0xu2c//v0BLFqfKrQ3e3nVYeSUVOHHvWqH730lDqqL8MovhwGYw3CT/3cQR7NL6rmLiIiIiIiIiIiISIqhHCIiIiIiapDwIE/c26MZfnzyjmuynptSgeeGtoari/SvJSoXBZ4Z1MrJXcCxuWOwJ2Y4nhwQhpERwUhfMA7/6dVCuH5/z+YAzFVt1LHR2DttBP5+cSAGtgnCtKgO+HZSL3z6UHeHay+Y0BkAMOvOjvBSuSBcFIxZ9UxfSbsri02vDbYbG9Q2SHLe2McaDLIEcs4sisLhWaPgqNvY+eIqDLZZAwC2vznU4b7FOs6Or/P6gNhtwvF793cTjjvPiccH8Wn1rg8An2497fSa7buL6WtbfnV7x9xayxKsAYCRH+2CRqvHm78fFcZWHchGZY01ZHQip1Q4/mhzOpYlZArnC+JOoffCLcgvrwYAlFfrEBYTh5dWHUZYTJzwLHHbMUf+PnoBD3y1D2uPXMCR7BKk5ZVjy6l8jF+6B21nWKsb7T5diHOXNHb3n8gpRVhMHI6drzvEozMYUVqlg95grHPelTAYTTDW854E/Lz/rBC+IiIiIiIiIiIiuh7YvoqIiIiIiG45i9afwje7MhHio0Li9JENuufbXZnw81DigV4tcKagAmGBnlA4SrzUSjpbhKd+PIStbwyBl8oFGq0BAZ6udvPu/nw3xnRqjBeGmSv4bD2Vh6f/Z/17izo22q5dlLg91bqXBqJzM18knC5AE193tAm2r4Bz12e7cVwUOFn9XH/0bOmPmX8dR25pNb58tCdc5DLIZDLhWZGhfkg+d3XVWxztHQDGdApB/Mm8K1rzl2f74qFv9gvn08Z1wOINqQgP8sSTA8Iwa+3Jy1pvYp9QPDsoHGFBng736shnD/fAS6vqD1ukzh8LN6VCMiZ+xicPdkd5jR6z/johjGUtjkJ2URUGv78dAHBs7mioXORQuShQWqUTAkcAkLFwnKSl1qmLZRj3aQJOLxyHtjM2CONXUnnK4r2NqfhPrxYIC/K0e4eEt4ahRYCHMP5+fCpa+Hvgod6hV/y8f5K62qQRERERERERERHVpaHtqxjKISIiIiKiW05GfjmiPt2NTa8NloQNbhXP/HQIm1PMoRV1bDRKNFrM+b+TWHvkgjD2wFd70TLQEx880K2upQTVOgPOF1dB5SKXBClsnbxQCqMR6NLcF3qDEct2Z6FYo8XXOzMl896/vyum/mFu/5S+YBwMRhMiZltbUX31aE+M7dwY21Lz8NSP0r+HbX5tMEZ9vEsyljp/LDrNiberNNO5mQ9O5FhbVR2dMxoF5dUY+ZH5/jOLoiThqIYGa2wNadcIO9MLruheZ7a+MQQZ+RWYsjwJb4/tgHc3ptrN+eKRSDy/IrnetXZNHSYEdWxZQh/O3t1ZKGTGn8exIvEcAKBfeCBm39URzf3d4e2mBAAcyS7BhKV7JGuUanToNs8cDJoyJFz4XWx5fQhGfrQTAJC5KAryOgJrN9rstSfQsYnPDQsLlVbpcP+Xe3E639wy7dS8sXB3tYazLP9OcrNbBhIRERERERER0a2LoRwiIiIiIqLrKOVCGVoFeUr+x/x3N6aiW3M/jO3c+Ibvp6hSi8j5mwEAM6MjMHlQOC5V1KCoUou2Id4AgBq9Ae1nmoM54iCIOCzSLsQL//fiQHSYZZ73v6d6w0ulQM+WAajWGfDCimS8Nqod2oV4S1qPLd+nxva0Anz/xB3CftJyy9GvdaBkn3vPFGLit4mSsUBPV1yq1Arn3Zr74vOJkRj0nuOQi62vHo3Ef3+uPzhzsygVMnz2cA+ne/zs4R64q1tTAObAiJfKBQq5zGGIp2dLf6x+rj8AIHpJAk5eMAeivno0EsM6BCN6yW5k1IZNnFkwoTOyCisRGeqPHqF+8HFXYuwnu/Dc0NaY8ecJfPJgd0zo0UyYX6M3ILuoCm2CvWAwmpBdpMHR8yUY372Z02c4q0JjMplQUF6D4NqWbiaTSagqda0q1lRpDTiSXWL32wOAp348iG2p+ZKx10a2w0O9WyCkdk+WvR+dPRq+HuYAVEZ+Ob7emYnfk85fUaipSmvAFzsy8Pqodrdc2KdaZ8Dhc44/LyIiIiIiIiIicoyhHCIiIiIion+ZrafykJpbLrTaaqjD54pxzxd7AViDCFq9EUqF7JoHCKq0BqFiz/Y3hyLERwUPVxchCLH+5UHo2NQHgOPKMmue748eLfwwa+0J/LzfXEXm12f7IryRF+5YuAXeKheU1+gBAEsnRqKxrwq+7q5ClZiG6BsegP2ZRZKxpJkj0XPBlgbd/3i/lvjfvrMNfh4AvDGqHT7cnC6c+7orUVqlczi3rso74vdvqHsjm2FNco7dM/QGIx79LlH4LMKDPJFZWCnM2TV1GEIDzVWdfjuYjWq9AeFBXhjYNkiytzGdQvD1Y71gMpkQvWQ3Ui6WCWPikFZdoZxPt5zGx1vMn88HD3TD/T2bO51r+7lY1j2RU4o7P9vt9D51bLQkJLTm+f6IDPV3uOaBGSMQ7O3mdC1ne3pxWBu8OaZ9nXNXJ53H0PaNEOilkoxrtHrIZTK7lmtX66NNaViyLQNvjGqHl0a0vaZrExERERERERH9UzU0lONyIzZDRERERERE19+IiBCMiAi57Pt6hPrbBSLEVXCuJXFloVai1mSP92sJT5WLEMgBgNMLx0Euk6F/7FbkldUAgBCSWDChixDK6dnSHy4KOdSx0TAaTUg6V4xeLf0lgaKsxVEArC2JnLWS6trcF1880hPuSoWk3VeglwqP9W2J5fvNYRulQgadwf7/ySXYW4V3xnfG+eIqbLWpyGKrTbCXUNVGHMgB4DSQU9feAVx2IAcAKqrt73H0DHEgBwBKqrTYuOsiFq2Xtv1KnT9Wch5/Ms9uvfiTeZi25jhWHTgnjBmMJkmrs6m/H0WxRodlj/cSAjkA8ObvR4VQzoJ1KXikb0vht2Q02n8nR7NLML62zVdddAajUHkIABIzi1BYXoPtafZt037Yo8bbYzsAkFb7SZk3BisTz2HR+lPIWGitqNOxiQ9SLpbh8+0ZeGO082o5Zy9V4o3fjwKwDyl1nB3vcPxqJWaZQ1erk88zlENEREREREREdI2xUg4RERERERHdUIfURXB3VaBTU9+buo/6qpeoCysx9IMd6NbcF2tfHCiMW6oIlVXpUVatw6D3tqNveABWTu4Lmcwa/Fl//CKeXyFtWfX1Yz0xZXkSfnjiDgzrEIyuc+NR5iAUI3Z64Ti0nbHhst5t7l0dMffvlMu653Itm9QLk3+y/zv8+pcHIWpJwhWtObFPKBbd0wUGowmtp5uDLrPv7Ih566TvEntvFwyPCEbvhVsBAKuf64+iSi2ecbAfZ9Sx0XUGnOrzwrDWWLr9TJ1zEt4ahhYBHpLnPN6vJd4Z3xnZRRrIZEBzfw/hWszqY/jlYLawPzHLGpb2WV/tPIO03HK8OaY9mvm5X/F7LIxLwbcJWQCAPTHDr2qthth44iICPFXo3Srgmq5bWqWDr7vymq5JREREREREROQM21cRERERERER1aFEo4XKRSGp3nM9JJ0txoGsIrQK8sTYzo0l1+b+30n8uFctnPt7KNHE1x0pF80VWyzBDK3eCIPRJKneAwD7pg1HldaA4R9K23NlLopC+PT18PdQIv7VwQj0UiF2wylM6heGd/4+iS2n8vFE/zDJsxtqWPtGDqvH3GwergpotIYGz1fHRuM/X+/Dgayi+ifX6tzMBydyyuqfKPL+/V0x9Y9jkrFfn+2LB7/ZDwC4L7I5PvxPNwDA2E92ITW3HIA0IFNZo0enOfHC/YdmjkQvUTu1uqrnlGi08HVXOq3OM2ftCaHdWpCXKw7NHHVZ72dRWFGD7CINetRWs3LGEi5KXzDumlXk2nfmEh7+dj9+fPIODG0ffE3WJCIiIiIiIiKqS0NDOdenHjkRERERERHRLc7Pw/W6B3IAc3ut54a2tgvkAMDR8yXC8fKne2Pjq4Ox/pVB+Oaxnvjucevf6V1d5HB3VWDrG0OEsaSZI9HE1x3hjbxwaOZIYfz+ns0hl8ugjo3G4dmjEezjBoVchhnRHdEiwAPLHr8D6thozL27k8P9JonWspUybwy+mWT/bw0bXhlkN/bayHYOwyKHbNZ/YVhrp8+z9fnEHk6vNTSQs3RiJLa8PhgA8NuUfshcFNWg+6K6NMa6l+zfsz62gRwAQiAHMLeN0uqNACAEcgBgQOw2nMgphcFowvY0aSs0cSAHAP4+egFqm/ZigDkQ1n3eZrSath4mkwkmkwmJmZfw4spkrD2Sg7CYOCGQAwDdW/hd9vuJ93TPF3txOs/6DptTzK3LdqYXIOlsEcZ/vlu4NqG2pZjBaEKvBZtRWqWDurASuaXVDX7m0z8exPJ9auzPvAQAeOKHg/hkS3rdNxERERERERER3UCslENERERERER0k1woqUL/2G34+ek+GNg2qEH31OgNUMrlkMutlU9MJhMe+mY/ErOKcGzuaPi4NbyNz+m8crQI8IBCLoNGa4CvuxKVNXoknytG1+Z+DlsC2bZ9UsdGIyO/AiM/slbsyVocJVRnWbT+FL7ZlYkZURF4ZnC4cP/ut4ehqa878str8NuhbHy0WRqoCA3wwLkiDQDguaGt8caodnjmp0OSSj0KuQwGo/XfNv58vj/u/XIvbP+549OHuiO7SIMXh7e1ex+t3oh2M60twn55ti8eEgVnACB51igEeLqiSmuwq1gEAG+MaocPNzsPhIjfxZGnBrTC93uynF5viH7hgWgd7Ikmvu54Pz4N7koFqnTmsNLaFwZgfG0Qxpari1wIBqljo6HVG1FWrUOQl8pu7rbUPMhlMvx2KBvrj+cCALa8PhgjP9olzFkxuQ+6NvdFl7mbnO61SzNf/DalHwa9tx2FFTWSa3VV/rHIyC8XnnlPj2b483CO5LqlzRcRERERERER0fXA9lVEREREREREdF1sPHER//05GQDQI9QPfz4/AACQU1KFvLJqRNbTwsiZsJg4NPd3R8y4Doju0gQymQxbUvLgqXLBHWH+cFHIcfJCKaKXmCuuTOrXEvPGd5aEhNSx0Yg/mYspy5Owb9pw9Fu8TRhviGqdAW5KawWlw+eK0czfHcHebnZ7tXBTynFq3li0mrYegDmQZDm2iO7SBEsfiZTc561yQXmNXjLvcttwNdT88Z0wa+3JeuetmNwHjyxLBAAsmNAZj/ZtKQSuJvVriZ9ElXWulwFtArFicl9cKKnCmYIKDGrbSLi28UQuujT3xYDYbXWu8UT/MKEalFZvRHpeOe78zFqpp6G/h3+K88UaKBVyhPiYf8eTvj+AAA8lPnnIefUpIiIiIiIiInKOoRwiIiIiIiIium6MRhOyLlWidSOvG/pcg9GE8Ut3Y3j7YLw+uj0AYPfpQjz6XSJaBnpg59RhN2QfOoMRBqMJSWeLERrggRYBHpLrs/46geX7z+LeHs3g467ErDs7QiGX4ePN6fh062kAwNE5o9HtHWk1mQPTR+DHvWp8sePMDXkPi1dGtBX2dSt4e2wHvLsxFQCQvmCcpJJRQ9VVHWj328Nqg0ZhCA3wQGSoP4wmE+78bDfmj++Ex/qFNegZ721Mhc5gxIzojpe9v2vp5/1nMfOvE9gTMxyF5TXoZtOKzBIGs4SRbM/1BiOeX5GMl4a3RZfmvnU+y2QyoVpnvCHt/4iIiIiIiIhuVQzlEBERERERERHdgnalF6Bf60AobSr/rJzcB/3bmNuYmUwmlFbp4OfhinXHLqBLM18083OHXCZD+HRrRZ4TOWWo1hvw4aY07M8suqL99AsPxF3dmmL6n8ev6r0OTB+B3ou21jvP30OJYo2uweu+NbY93tuYdjVbu2xrXxiA9LxyTP3jGLxVLjj+zhgAwPL9ZzHrrxN4dWRb1OiN+FIUngoP8kRmYSUAIO7lgYheshup88fi5/1ncVe3pkKVGotlCZno3sIPvcICUFatg6erC/5IykaQlwojIkKQfK4Y936xF08PbIVOTX3w+m9Hsfq5fujZMgArEs9iVMcQBHu7IeVCGaKWJEjWTp0/Vqj4pDcY0WaGOdS0cnIfRLb0R4dZ5hZsGQvHwUUht6s25UhGfjlKq/S478u9AID900agsa+bw7lXwmQyCS3vrpTBaILiNmhbpjMY8ebvR/HMoHB0blZ3CIqIiIiIiIhuTQzlEBE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      "text/plain": [
       "<Figure size 2880x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "l.save(f\"r_speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}_log_{log}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "old_models\r\n",
      "old_repr\r\n",
      "r_speedup_Adam_batch_norm_True_MAPE_nlayers_5_log_False.pth\r\n",
      "speedup_Adam_batch_norm_True_MAPE_nlayers_5_log_False2.pth\r\n",
      "speedup_Adam_batch_norm_True_MAPE_nlayers_5_log_False.pth\r\n",
      "speedup_Adam_batch_norm_True_MSE_nlayers_5_log_False.pth\r\n",
      "speedup_Adam_batch_norm_True_MSE_nlayers_5_log_True.pth\r\n",
      "tmp.pth\r\n"
     ]
    }
   ],
   "source": [
    "!ls models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_df = get_results_df(val_dl, l.model)\n",
    "train_df = get_results_df(train_dl, l.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>245283.000000</td>\n",
       "      <td>245283.000000</td>\n",
       "      <td>2.452830e+05</td>\n",
       "      <td>245283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.102805</td>\n",
       "      <td>1.135724</td>\n",
       "      <td>2.135267e-01</td>\n",
       "      <td>22.577551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.295271</td>\n",
       "      <td>1.405682</td>\n",
       "      <td>5.665563e-01</td>\n",
       "      <td>91.278419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010044</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>2.048910e-07</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.280880</td>\n",
       "      <td>0.278690</td>\n",
       "      <td>5.817745e-03</td>\n",
       "      <td>1.646686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.855459</td>\n",
       "      <td>0.899071</td>\n",
       "      <td>3.095371e-02</td>\n",
       "      <td>5.807508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.050393</td>\n",
       "      <td>1.036481</td>\n",
       "      <td>1.352153e-01</td>\n",
       "      <td>17.479273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.452207</td>\n",
       "      <td>16.089287</td>\n",
       "      <td>1.541526e+01</td>\n",
       "      <td>5824.463867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          prediction         target      abs_diff            APE\n",
       "count  245283.000000  245283.000000  2.452830e+05  245283.000000\n",
       "mean        1.102805       1.135724  2.135267e-01      22.577551\n",
       "std         1.295271       1.405682  5.665563e-01      91.278419\n",
       "min         0.010044       0.008491  2.048910e-07       0.000030\n",
       "25%         0.280880       0.278690  5.817745e-03       1.646686\n",
       "50%         0.855459       0.899071  3.095371e-02       5.807508\n",
       "75%         1.050393       1.036481  1.352153e-01      17.479273\n",
       "max         8.452207      16.089287  1.541526e+01    5824.463867"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.176222</td>\n",
       "      <td>1.430897</td>\n",
       "      <td>0.442006</td>\n",
       "      <td>42.532043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.231279</td>\n",
       "      <td>1.683147</td>\n",
       "      <td>0.734061</td>\n",
       "      <td>148.393097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.014810</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.006628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.389919</td>\n",
       "      <td>0.395193</td>\n",
       "      <td>0.024651</td>\n",
       "      <td>4.690779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.961758</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.134899</td>\n",
       "      <td>22.480320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.257520</td>\n",
       "      <td>1.621683</td>\n",
       "      <td>0.497272</td>\n",
       "      <td>48.398965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.889497</td>\n",
       "      <td>10.872228</td>\n",
       "      <td>6.232839</td>\n",
       "      <td>5399.246094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         prediction        target      abs_diff           APE\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000\n",
       "mean       1.176222      1.430897      0.442006     42.532043\n",
       "std        1.231279      1.683147      0.734061    148.393097\n",
       "min        0.014810      0.014795      0.000040      0.006628\n",
       "25%        0.389919      0.395193      0.024651      4.690779\n",
       "50%        0.961758      1.000000      0.134899     22.480320\n",
       "75%        1.257520      1.621683      0.497272     48.398965\n",
       "max        7.889497     10.872228      6.232839   5399.246094"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[:][['index','name','prediction','target', 'abs_diff','APE']].to_csv(path_or_buf='./eval_results.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2074.000000</td>\n",
       "      <td>2074.0</td>\n",
       "      <td>2074.000000</td>\n",
       "      <td>2074.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.998558</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050076</td>\n",
       "      <td>5.007599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.208153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.202042</td>\n",
       "      <td>20.204212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.603882</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.006628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.979231</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>0.255150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.995987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004715</td>\n",
       "      <td>0.471514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.997637</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022805</td>\n",
       "      <td>2.280509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.001675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.001675</td>\n",
       "      <td>500.167511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction  target     abs_diff          APE\n",
       "count  2074.000000  2074.0  2074.000000  2074.000000\n",
       "mean      0.998558     1.0     0.050076     5.007599\n",
       "std       0.208153     0.0     0.202042    20.204212\n",
       "min       0.603882     1.0     0.000066     0.006628\n",
       "25%       0.979231     1.0     0.002551     0.255150\n",
       "50%       0.995987     1.0     0.004715     0.471514\n",
       "75%       0.997637     1.0     0.022805     2.280509\n",
       "max       6.001675     1.0     5.001675   500.167511"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.462764</td>\n",
       "      <td>1.519225</td>\n",
       "      <td>0.393448</td>\n",
       "      <td>29.481096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.346193</td>\n",
       "      <td>1.422773</td>\n",
       "      <td>0.573493</td>\n",
       "      <td>31.370106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.145803</td>\n",
       "      <td>0.091200</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.009375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.494025</td>\n",
       "      <td>0.508859</td>\n",
       "      <td>0.070696</td>\n",
       "      <td>8.597638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.991690</td>\n",
       "      <td>0.993141</td>\n",
       "      <td>0.191803</td>\n",
       "      <td>19.992519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.151304</td>\n",
       "      <td>2.207209</td>\n",
       "      <td>0.496262</td>\n",
       "      <td>41.369717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.768654</td>\n",
       "      <td>9.358214</td>\n",
       "      <td>3.835224</td>\n",
       "      <td>295.724640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff         APE\n",
       "count  725.000000  725.000000  725.000000  725.000000\n",
       "mean     1.462764    1.519225    0.393448   29.481096\n",
       "std      1.346193    1.422773    0.573493   31.370106\n",
       "min      0.145803    0.091200    0.000040    0.009375\n",
       "25%      0.494025    0.508859    0.070696    8.597638\n",
       "50%      0.991690    0.993141    0.191803   19.992519\n",
       "75%      2.151304    2.207209    0.496262   41.369717\n",
       "max      7.768654    9.358214    3.835224  295.724640"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>281.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.372236</td>\n",
       "      <td>6.044158</td>\n",
       "      <td>1.848176</td>\n",
       "      <td>66.339111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.171317</td>\n",
       "      <td>3.221366</td>\n",
       "      <td>1.231329</td>\n",
       "      <td>163.969162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.904279</td>\n",
       "      <td>0.105179</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>0.060693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.006489</td>\n",
       "      <td>2.546717</td>\n",
       "      <td>1.032713</td>\n",
       "      <td>18.170967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.899143</td>\n",
       "      <td>7.389543</td>\n",
       "      <td>1.563098</td>\n",
       "      <td>31.399256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.972240</td>\n",
       "      <td>8.668690</td>\n",
       "      <td>2.789691</td>\n",
       "      <td>43.133614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.070222</td>\n",
       "      <td>10.872228</td>\n",
       "      <td>4.899988</td>\n",
       "      <td>848.387634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff         APE\n",
       "count  281.000000  281.000000  281.000000  281.000000\n",
       "mean     4.372236    6.044158    1.848176   66.339111\n",
       "std      2.171317    3.221366    1.231329  163.969162\n",
       "min      0.904279    0.105179    0.001005    0.060693\n",
       "25%      1.006489    2.546717    1.032713   18.170967\n",
       "50%      5.899143    7.389543    1.563098   31.399256\n",
       "75%      5.972240    8.668690    2.789691   43.133614\n",
       "max      6.070222   10.872228    4.899988  848.387634"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.913173</td>\n",
       "      <td>0.918692</td>\n",
       "      <td>0.347373</td>\n",
       "      <td>122.910507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.018806</td>\n",
       "      <td>1.168210</td>\n",
       "      <td>0.588294</td>\n",
       "      <td>610.275146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.084280</td>\n",
       "      <td>0.018092</td>\n",
       "      <td>0.001015</td>\n",
       "      <td>0.220132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.260828</td>\n",
       "      <td>0.265846</td>\n",
       "      <td>0.033345</td>\n",
       "      <td>7.204351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.456005</td>\n",
       "      <td>0.466655</td>\n",
       "      <td>0.101908</td>\n",
       "      <td>19.017271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.092822</td>\n",
       "      <td>0.956079</td>\n",
       "      <td>0.327444</td>\n",
       "      <td>41.838287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.042413</td>\n",
       "      <td>8.069739</td>\n",
       "      <td>3.409136</td>\n",
       "      <td>5399.246094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff          APE\n",
       "count  232.000000  232.000000  232.000000   232.000000\n",
       "mean     0.913173    0.918692    0.347373   122.910507\n",
       "std      1.018806    1.168210    0.588294   610.275146\n",
       "min      0.084280    0.018092    0.001015     0.220132\n",
       "25%      0.260828    0.265846    0.033345     7.204351\n",
       "50%      0.456005    0.466655    0.101908    19.017271\n",
       "75%      1.092822    0.956079    0.327444    41.838287\n",
       "max      5.042413    8.069739    3.409136  5399.246094"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>868.000000</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>868.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.589693</td>\n",
       "      <td>1.748770</td>\n",
       "      <td>0.590289</td>\n",
       "      <td>37.538128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.585954</td>\n",
       "      <td>1.605983</td>\n",
       "      <td>0.793788</td>\n",
       "      <td>50.443039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.168853</td>\n",
       "      <td>0.057130</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.043231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.442154</td>\n",
       "      <td>0.695116</td>\n",
       "      <td>0.105190</td>\n",
       "      <td>12.070397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.012416</td>\n",
       "      <td>1.241629</td>\n",
       "      <td>0.302328</td>\n",
       "      <td>29.301636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.136123</td>\n",
       "      <td>2.229650</td>\n",
       "      <td>0.773482</td>\n",
       "      <td>44.885977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.889497</td>\n",
       "      <td>10.137201</td>\n",
       "      <td>4.684408</td>\n",
       "      <td>414.385712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff         APE\n",
       "count  868.000000  868.000000  868.000000  868.000000\n",
       "mean     1.589693    1.748770    0.590289   37.538128\n",
       "std      1.585954    1.605983    0.793788   50.443039\n",
       "min      0.168853    0.057130    0.000085    0.043231\n",
       "25%      0.442154    0.695116    0.105190   12.070397\n",
       "50%      1.012416    1.241629    0.302328   29.301636\n",
       "75%      2.136123    2.229650    0.773482   44.885977\n",
       "max      7.889497   10.137201    4.684408  414.385712"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1276.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.962418</td>\n",
       "      <td>2.878316</td>\n",
       "      <td>1.036611</td>\n",
       "      <td>72.382378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.334146</td>\n",
       "      <td>2.108479</td>\n",
       "      <td>1.021500</td>\n",
       "      <td>274.414642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.101093</td>\n",
       "      <td>0.042484</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.041299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.819918</td>\n",
       "      <td>0.977715</td>\n",
       "      <td>0.154067</td>\n",
       "      <td>10.751699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.072164</td>\n",
       "      <td>2.629361</td>\n",
       "      <td>0.717993</td>\n",
       "      <td>40.390493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.634203</td>\n",
       "      <td>4.365878</td>\n",
       "      <td>1.818399</td>\n",
       "      <td>50.273042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.919624</td>\n",
       "      <td>9.784180</td>\n",
       "      <td>5.172597</td>\n",
       "      <td>2245.031982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  1276.000000  1276.000000  1276.000000  1276.000000\n",
       "mean      1.962418     2.878316     1.036611    72.382378\n",
       "std       1.334146     2.108479     1.021500   274.414642\n",
       "min       0.101093     0.042484     0.000331     0.041299\n",
       "25%       0.819918     0.977715     0.154067    10.751699\n",
       "50%       2.072164     2.629361     0.717993    40.390493\n",
       "75%       2.634203     4.365878     1.818399    50.273042\n",
       "max       4.919624     9.784180     5.172597  2245.031982"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1663.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.827254</td>\n",
       "      <td>0.937209</td>\n",
       "      <td>0.295498</td>\n",
       "      <td>32.696384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.851419</td>\n",
       "      <td>1.023356</td>\n",
       "      <td>0.457449</td>\n",
       "      <td>32.905022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.014810</td>\n",
       "      <td>0.014836</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.030472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.217906</td>\n",
       "      <td>0.246893</td>\n",
       "      <td>0.038760</td>\n",
       "      <td>9.840522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.529727</td>\n",
       "      <td>0.624512</td>\n",
       "      <td>0.132425</td>\n",
       "      <td>24.348541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.120389</td>\n",
       "      <td>1.173670</td>\n",
       "      <td>0.354944</td>\n",
       "      <td>48.979925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.983345</td>\n",
       "      <td>8.724252</td>\n",
       "      <td>5.141850</td>\n",
       "      <td>309.855896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  1663.000000  1663.000000  1663.000000  1663.000000\n",
       "mean      0.827254     0.937209     0.295498    32.696384\n",
       "std       0.851419     1.023356     0.457449    32.905022\n",
       "min       0.014810     0.014836     0.000083     0.030472\n",
       "25%       0.217906     0.246893     0.038760     9.840522\n",
       "50%       0.529727     0.624512     0.132425    24.348541\n",
       "75%       1.120389     1.173670     0.354944    48.979925\n",
       "max       4.983345     8.724252     5.141850   309.855896"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2881.000000</td>\n",
       "      <td>2881.000000</td>\n",
       "      <td>2881.000000</td>\n",
       "      <td>2881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.670118</td>\n",
       "      <td>0.858291</td>\n",
       "      <td>0.383388</td>\n",
       "      <td>57.996346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.757664</td>\n",
       "      <td>1.094979</td>\n",
       "      <td>0.572025</td>\n",
       "      <td>79.790131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.016312</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.131162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.158396</td>\n",
       "      <td>0.183774</td>\n",
       "      <td>0.059443</td>\n",
       "      <td>19.831461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.420433</td>\n",
       "      <td>0.424563</td>\n",
       "      <td>0.154730</td>\n",
       "      <td>43.728092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.847958</td>\n",
       "      <td>1.033311</td>\n",
       "      <td>0.456153</td>\n",
       "      <td>58.873505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.961184</td>\n",
       "      <td>9.207530</td>\n",
       "      <td>6.232839</td>\n",
       "      <td>1148.485840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  2881.000000  2881.000000  2881.000000  2881.000000\n",
       "mean      0.670118     0.858291     0.383388    57.996346\n",
       "std       0.757664     1.094979     0.572025    79.790131\n",
       "min       0.016312     0.014795     0.000374     0.131162\n",
       "25%       0.158396     0.183774     0.059443    19.831461\n",
       "50%       0.420433     0.424563     0.154730    43.728092\n",
       "75%       0.847958     1.033311     0.456153    58.873505\n",
       "max       4.961184     9.207530     6.232839  1148.485840"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7926.000000</td>\n",
       "      <td>7926.000000</td>\n",
       "      <td>7926.000000</td>\n",
       "      <td>7926.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.222709</td>\n",
       "      <td>1.543651</td>\n",
       "      <td>0.544563</td>\n",
       "      <td>52.351154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.375149</td>\n",
       "      <td>1.874331</td>\n",
       "      <td>0.786424</td>\n",
       "      <td>164.959198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.014810</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.009375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.304365</td>\n",
       "      <td>0.312547</td>\n",
       "      <td>0.065765</td>\n",
       "      <td>12.863225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.714742</td>\n",
       "      <td>0.830516</td>\n",
       "      <td>0.205677</td>\n",
       "      <td>34.351240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.788819</td>\n",
       "      <td>2.132191</td>\n",
       "      <td>0.660448</td>\n",
       "      <td>52.750795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.889497</td>\n",
       "      <td>10.872228</td>\n",
       "      <td>6.232839</td>\n",
       "      <td>5399.246094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  7926.000000  7926.000000  7926.000000  7926.000000\n",
       "mean      1.222709     1.543651     0.544563    52.351154\n",
       "std       1.375149     1.874331     0.786424   164.959198\n",
       "min       0.014810     0.014795     0.000040     0.009375\n",
       "25%       0.304365     0.312547     0.065765    12.863225\n",
       "50%       0.714742     0.830516     0.205677    34.351240\n",
       "75%       1.788819     2.132191     0.660448    52.750795\n",
       "max       7.889497    10.872228     6.232839  5399.246094"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange + df.tile + df.unroll != 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/scratch/henni-mohammed/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0su+++zJz5kw15TUzDV8kIlKHf/zjHyxYsACAMWPG0KZNm4AjalmUnEREatm+fTtXXnklN954Y9ChtFhq1hMRqaV169a88MILdOzYMehQWizVnEREYqZPn87VV1+Nu9OrVy/y8/ODDqnFUnISEYmZM2cOs2fPZvv27UGH0uKpWU9EWrzKykqysrL44x//WH0/kwRLNScRadFmz57N4YcfTklJCVlZWWrKCwklJxFp0bKysohEIkQikaBDkRqUnESkRdq0aRMAxx57LK+88gqdOnUKOCKpKZBp2s3sOjN7x8wWmtmzZtYj1XGIiFR544036Nu3b/Xo4mYWcERSW1DTtN/i7oPc/WBgJnHO7SQikgwDBgzgxBNP5KCDDgo6FKlHINO0u/vmGk/z0VTtItIMli1bRkVFBR07duSBBx6ga9euDb9IAhHYNSczu8HM1gIT2EPNSVMWi0gyrF+/nqFDh3LFFVcEHcoe6ZwXFVhycvcr3H1v4CHgwj3spymLRSRhPXr0YPLkyVx4Yb2nm1DQOS8qDL31pgHfDjoIEclMRUVFrFy5EoAf//jH9OrVK+CIJB6BjBBhZvu5+4rY07HA0iDiEJHMVlFRwZlnnklhYSEvv/yyeuWlkZQnp9g07SOBLma2DrgKON7MBgCVwEfAj1Idh4i0PJFIhMcff5z8/HwlpjST8uTk7mfVsfreOtaJiCTFu+++y7x58/je977HgQceGHQ40gRhuOYkIpJUU6ZM4f/9v//H5s2bG95ZQkmjkotIxrn77rtZv3497du3DzoUaSLVnEQkI6xcuZIJEyawdetWWrVqRd++fYMOSRKg5CQiGWHhwoXMnTuXtWvXBh2KJIGSk4iktcrKSgBOPfVUVqxYwcCBAwOOSJJByUlE0taaNWsYPHgwr7/+OgDt2rULOCJJFiUnEUlbWVlZZGdnk5OTE3QokmTqrSciaWfTpk20b9+eXr168eabb+oG2wykmpOIpJXPP/+cYcOG8ZvfRCczUGLKTEpOIpJWCgoKOOmkkxg9enTQoUgKNcfYevcBJwIb3P2A2LpbgJOAHcAHwHfdvTTVsYhI+iopKcHd6dq1K7fcckvQ4UiKBTVN+xzgAHcfBCwHLm+GOEQkTbk748aN4/jjj6/uOi6ZrTkGfn3JzPrWWvdsjadvAKemOg4RSV9mxk033cSOHTvIytLViJYgDL/l7wGz6tuoKYtFWq7S0lLmzJkDwFFHHcWxxx4bcESpp3NeVKDJycyuAHYSnaq9TpqyWKTl+vWvf83JJ59MSzpJ65wXFVhyMrNziXaUmODuHlQcIhJekydPZubMmbTkk3RLFUhyMrMxwP8BY919WxAxiEg4bdmyhWuvvZby8nLat2/PyJEjgw5JApDy5BSbpv11YICZrTOz7wN/BNoBc8xsoZndneo4RCQ9zJw5k2uvvZZ58+YFHYoESNO0i0ionHXWWQwePJivfOUrQYciAQpDbz0RaeHKysqYOHEiS5cuBVBiEiUnEQlecXExc+fOpaioKOhQJCQ0KrmIBKayspKsrCz23Xdfli9fTtu2bYMOSUJCNScRCcSOHTs4+eST+d3vfgegxCS7UHISkUCYGa1ataJNmzZBhyIhpGY9EWlW5eXlbN++nbZt2/Loo49qPiapk2pOItKsvvvd7zJmzBjKy8uVmKReqjmJSLMaP348a9asIScnJ+hQJMSUnEQk5SoqKli+fDn7778/p56qGXKkYWrWE5GUu+aaa/j617/ORx99FHQokiaCmqb9NOBqYH9gqLsvSHUcIhKcCy64gG7dutGnT5+gQ5E0EdQ07UuAU4CXmuH4IhIAd+exxx7D3enevTs/+clPgg5J0kjKk5O7vwRsrLXufXdflupji0hwpk+fzumnn85TTz0VdCiShkJ/zUlTFoukp5NPPpmZM2dy0kknBR1KWtE5Lyr0yUlTFoukD3fn1ltvZf369ZgZJ5xwgu5laiSd86JCn5xEJH2sXr2aa665hvvuuy/oUCTN6T4nEUmafv36UVRUxD777BN0KJLmApmm3cxONrN1wGHA02Y2O9VxiEjqXHfddfzjH/8AYN9991VTniQsqGnaAaan+tgikno7duzgueeeY82aNZx55plBhyMZQs16ItJklZWVtGrVilmzZtG6deugw5EMog4RItIkt956K6eccgo7duwgLy+PSCQSdEiSQZScRKRJ2rRpQ25uLllZOo1I8umvSkQapbS0FIALL7yQadOmkZ2tqwOSfEpOIhK3v/3tbwwYMICVK1cCqFeepIySk4jEbcSIEYwbN47evXsHHYpkOCUnEWnQkiVLANhvv/2YOnUqrVq1CjgiyXRKTiKyRy+88AKDBg2qvslWpDkoOYnIHh1xxBHcfPPNjBs3LuhQpAVRchKROs2ePZtNmzaRnZ3NpZdeSm5ubtAhSQvSHGPr3WdmG8xsSY11ncxsjpmtiP3smOo4RCR+n376KSeffDJXXnll0KFICxXUNO2XAXPdfT9gbuy5iIREt27deOaZZ7jpppuCDkVaqECmaQfGAffHHt8PjE91HCLSsCeffJK5c+cCMHLkSNq2bRtwRNJSBXVrdzd3/xjA3T82s6717Whm5wPnA7q3QiSFKisrufbaa2nbti1HH320brANiM55UaEfd8TdpwJTAYYMGeIBhyOSsbKysvj3v/9Ndna2ElOAdM6LCqq33qdmthdA7OeGgOIQafHmzJnDxRdfTGVlJV26dKGgoCDokEQCS05PAufGHp8L/CugOERavFdeeYUXXniBLVu2BB2KSLVApmkHJgPHmtkK4NjYcxFpRpWVlQBcffXVvPbaa3To0CHgiET+pzl6653l7nu5e46793L3e939v+4+yt33i/2s3ZtPRFLolVde4ZBDDmHNmjWYGfn5+UGHJLILjRAh0gK1atWKvLw8DeAqoRX63noikjylpaUUFBQwdOhQXnvtNfXKk9BSzUmkhViyZAn9+/fnscceAzRRoISbkpNIC9G/f39OPvlkhg0bFnQoIg1Ss55Ihlu+fDl9+vQhLy+Pe++9N+hwROKimpNIBistLeWII47gggsuCDoUkUZRzUkkgxUUFDBlyhQOO+ywoEMRaRQlJ5EMtHTpUr788ksOPvhgJk6cGHQ4Io2m5CSSYdyd7373u2zatInFixcTiUSCDkmk0ZScRDKMmTFt2jTKysqUmCRtBdohwswuNrMlZvaumV0SZCwi6W7VqlXcfvvtAPTr14+vfvWrAUck0nSBJSczOwCYBAwFDgJONLP9gopHJN39+c9/5rrrruOTTz4JOhSRhAVZc9ofeMPdt7n7TuBF4OQA4xFJazfeeCPz58+ne/fuQYcikrAgk9MS4Egz62xmecDxwN61dzKz881sgZktKCkpafYgRcJs3bp1nHbaaWzcuJFIJEL//v2DDkkSpHNeVGDJyd3fB24G5gD/BhYBO+vYb6q7D3H3IYWFhc0cpUi4LVu2jJdeeonVq1cHHYokic55UYH21nP3e4F7AczsRmBdkPGIpIvKykqysrIYNWoUq1at0nxMknGC7q3XNfazN3AK8HCQ8Yikg08//ZQhQ4Ywa9YsACUmyUhB3+f0TzPrDJQDP3H3zwOORyT0cnJyaNu2LXl5eUGHIpIyQTfrfSPI44ukk02bNtGuXTs6derEiy++qPmYJKNpVHKRNFBWVsbIkSOrRxdXYpJMF3SznojEITc3l1NPPZXBgwcHHYpIs1ByEgmxTZs2UVpaSp8+fbjiiiuCDkek2ahZTyTEJkyYwKhRo9ixY0fQoYg0K9WcRELs+uuv56OPPqJVq1ZBhyLSrFRzEgmZrVu38uSTTwJw8MEHM27cuIAjEml+Sk4iITN58mS+/e1vs2rVqqBDEQmMmvVEQuaKK65g5MiRGsRVWjTVnERC4Msvv+Sqq65i27ZttGnThlGjRgUdkkiglJxEQuCll17ihhtu4Pnnnw86FJFQCLRZz8x+BvwAcGAx8F13/zLImESCcNxxx7Fs2TL22WefoEMRCYUgp2nvCfwUGOLuBwAR4Myg4hFpbuXl5Zx33nm8+eabAEpMIjUE3ayXDeSaWTaQB6wPOB6RZvPZZ5/xyiuv8PbbbwcdikjoBNas5+7FZnYrsAYoA55192dr72dm5wPnA/Tu3bt5gxRJgcrKSsyMvfbai0WLFmk+JtmFznlRQTbrdQTGAf2AHkC+mX2n9n6aslgySUVFBRMnTqweJ0+JSWrTOS8qyGa9Y4AP3b3E3cuBJ4DDA4xHJOXMjHbt2tG+ffugQxEJtSB7660BhptZHtFmvVHAggDjEUmZyspKtmzZQocOHbjrrrs0H5NIAwKrObn7POBx4G2i3cizgKlBxSOSShdffDHf+MY32Lp1qxKTSByCnqb9KuCqIGMQaQ7jx4+nsLCQvLy8oEMRSQtxJSczaw18G+hb8zXufm1qwhJJf+7O4sWLGTRoEKNGjdKQRCKNEG+z3r+I9qzbCWytsYhIPX7/+98zZMgQFi9eHHQoImkn3ma9Xu4+JqWRiGSY8847j6ysLA444ICgQxFJO/HWnF4zswNTGolIBnB3Hn30UXbu3ElBQQE//elP1QFCpAniTU5HAG+Z2TIze8fMFpvZO6kMTCQdvfzyy5xxxhk88MADQYciktbibdb7VkqjEMkQRx55JLNmzeK4444LOhSRtBZXzcndPwIKgJNiS0FsnYgQ7fywYsUKAMaMGUNWVtBjKoukt7j+g8zsYuAhoGtsedDMLkplYCLpoqSkhBtuuIG777476FBEMka8zXrfB4a5+1YAM7sZeB34Q6oCE0kXhYWFzJ8/n169egUdikjGiLftwYCKGs8rYutEWqzbb7+dP/7xjwD06dOHSCQScEQimSPe5PRXYJ6ZXW1mVwNvAPcmcmAzG2BmC2ssm83skkTKFGkulZWVvPjii7zwwgu4e9DhiGScuJr13P02M3uBaJdyA77r7kWJHNjdlwEHA5hZBCgGpidSpkhzqKioIBKJ8Mgjj2Bmuo9JJAX2WHMys/axn52A1cCDwAPAR7F1yTIK+EA9ACXs7rnnHo455hi++OILWrVqRU5OTtAhiWSkhmpO04ATgbeAmm0XFnveP0lxnAk8XNcGTVksYdKuXTvatWunpCQpo3NelAXdXm5mrYD1wNfc/dM97TtkyBBfsEDzEUrz27hxI506RRsL3F1NeZKouP6AMvScF9d7j/c+p7nxrGuibwFvN5SYRIIyffp0+vfvT1FR9DKrEpNI6u2xWc/M2gB5QBcz68j/Ml57oEeSYjiLepr0RMJg6NChnHbaaQwYMCDoUERajIZqTj8ker1pYOxn1fIv4E+JHtzM8oBjgScSLUsk2RYtWoS707NnT+655x7NYivSjPaYnNz99+7eD/ilu/d3936x5SB3/2OiB3f3be7e2d03JVqWSDItXLiQwYMHc+eddwYdikiLFO9NuJVmVlD1xMw6mtkFKYpJJHAHHXQQt912GxMnTgw6FJEWKd7kNMndS6ueuPvnwKTUhCQSnLlz5/LJJ59gZvz0pz+lffv2QYck0iLFm5yyrEYXpdiIDq1SE5JIMLZs2cLpp5/Oz3/+86BDEWnx4h2VfDbwqJndTfTm2x8B/05ZVCIBaNeuHbNmzWLfffcNOhSRFi/e5PR/RHvu/Zhod/Jngb+kKiiR5vT888+zceNGvv3tbzN06NCgwxER4h/4tRK4K7aIZAx3Z/LkyWzYsIFx48aRnR3v9zURSaWGbsJ91N1PN7PF7Dq2HgDuPihlkYk0AzPj8ccfp6ysTIlJJEQa+m+8OPbzxFQHItKcXnvtNe655x6mTp1aPZiriITHHpOTu38c+6mpLCSjvP3227z66qt8/vnndO3aNehwRKSWhuZz2hKbobbOJdGDm1mBmT1uZkvN7H0zOyzRMkX2pKKiAoALL7yQhQsXKjGJhFRDwxe1c/f2wO3AZUBPoBfR3nvXJ+H4vwf+7e4DgYOA95NQpkidioqKOPDAA3nvvfcANFaeSIjFewV4tLsPq/H8LjObB/y2qQeOzbJ7JHAegLvvAHY0tTyRhuTm5tKhQwclJZE0EO8IERVmNsHMImaWZWYTgIoEj90fKAH+amZFZvbeNhrsAAAgAElEQVQXM8tPsEyR3WzcuBGAgQMH8tprr9G3b99gAxKRBsWbnM4GTgc+jS2nxdYlIhs4FLjL3Q8BthJtOtyFmZ1vZgvMbEFJSUmCh5SW5sMPP2T//ffnrruit+hpokAJO53zouJKTu6+2t3HuXsXdy909/HuvjrBY68D1rn7vNjzx4kmq9rHnuruQ9x9SGFhYYKHlJamV69enHbaaYwaNSroUETionNeVLzTtH/FzOaa2ZLY80FmdmUiB3b3T4C1ZlY1vego4L1EyhSp8sEHH7B582ZycnL44x//yFe+8pWgQxKRRoi3We8e4HKgHMDd3wHOTMLxLwIeMrN3gIOBG5NQprRw27dv55hjjtFcTCJpLN7eennu/mat9vqdiR7c3RcCQxItR6Sm1q1bc/vtt9O/f/+gQxGRJoo3OX1mZvsQG1/PzE4FPk5ZVCJNsHr1atatW8cRRxzBuHHjgg5HRBIQb3L6CTAVGGhmxcCHwISURSXSBFWjPqxcuZI2bdoEHY6IJKDB5GRmWcAQdz8mdh9SlrtvSX1oIo1z3333UVxcrMQkkgEa7BARm8vpwtjjrUpMEibr16/nhhtuwN3p2rUrhxxySNAhiUgSxNtbb46Z/dLM9jazTlVLSiMTicO0adOYPHkyH3zwQdChiEgSmftucwjuvpPZh9Q92WCzdocaMmSIL1iwoDkPKSHn7qxevZp+/foFHYpIY8Q1VEmGnvPieu/x1py+CvwJWAQsBP4AfK1pcYkkpqSkhJNPPpl169ZhZkpMIhko3uR0P7A/cAfRxLR/bJ1Is/voo494/fXXWbVqVdChiEiKxNuVfIC7H1Tj+fNmtigVAYnUp6KigkgkwpAhQ1i1apWmvhDJYPHWnIrMbHjVEzMbBryampBEdldaWsqIESOYNm0aoIkCRTJdvDWnYcA5ZrYm9rw38L6ZLQbc3Qc15eBmthrYQnRuqJ3urqGMpE45OTl07NiRDh06BB2KiDSDeJPTmBTG8E13/yyF5Usa27JlC61btyY/P59nnnlG8zGJtBBxJSd3/yjVgUjLNKOomFtmL2N9aRkFeTls3b6THRXRuxa8soJPH76crLwOFI7/dbMkJjNwh54FuVw6egDjD+m5S4w9aqwXkdSJt+aUKg48a2YO/Nndp9bewczOB84H6N27dzOHJ8lQOwG5w6aycjrk5rB1x07KY8no823lu7zOsiLkf3UkWbntm63GVHXbX3FpGZc/sZgFH23kn28VU1Zesct6QAlKUkLnvKi4bsJN2cHNerj7ejPrCswBLnL3l+rbP0NvSMtoM4qKufyJxdUn93hUln9JxebPyOncK4WRxSdiRkUd/yM9C3J59bKjA4hIMoRuwm1AoDUnd18f+7nBzKYDQ4F6k5Okn1tmL2tUYgLY+OydlK16m57nTyWrdbC98upKTADrS8uaORJpiTZu3cG0eWsa3jFBZw8LXw0tsORUc4Tz2OPjgGuDikdSoykn8Q6Hn0lu/yGBJyaov+bUoyA3gGhEWo4ga07dgOmxawnZwDR3/3eA8UgK9CjIpTiOBOU7y9m24g3y9/8GOR17kNOxRzNEt2dGtOZk7D6w5LYdO5lRVFx93enKGYt5eN7a6P4GOVlW3bGjzrIN9i3MZ2XJVuprWa/qlAFwzVPv7nJNriA3h6vHfm2XDhvFpWXVybSnOm5Imgv0mlNjZWj7a0Zr6JpTTpbRtk02H730OJ/PvYe9zvs9rbrt08xR7q6uhFRbTsTIyTK2lVc2R0h1at86wpbtFXXGWvUe6ktU6oUYqLiuu/Tff5Bf/7eZqY6luZv1wn/NSTJf1cmurt56NU+IFRWjuP6eo3m2tOtuJ8sZRcVc/eS7lJb9r+ZQ1eW7IREzhvfvyOr/llWX+82BhTy/tITi0rJdyqmqjVTVQhpSXuHVPQ2Dsnl7/dfzqiIrLi3j0scWcc1T71K6rbz6M1AvRAkz1ZwkMOXl5Vx77bX84he/oKCgIOhwqvW77OkGa03prr6aoXohNhvVnBqgmpMEpqioiN/+9rcMGDCA73znO0GHUy3e62TprL7kq16I4RfGnnWpEO/AryJJN3ToUJYuXRqqxARw6egB5OZEgg4jEOqFKGGh5CTNqqKigh/+8IfMmTMHIJQTBY4/pCc3nXIgPQtyMaLXrTJR7XeVmxOp7h0oEjQlJ2lWW7ZsYd68ebz11ltBh7JH4w/pyauXHc2Hk09gyukHpWVNqir5FOTmkBPZNRXl5kSYMLx3dQLuWZDLTaccqM4QEhq65iTNorIy2t26oKCA119/ndzc9Gk+qt3jsKq328xFH1f3IMxvFSEnklXdC3FL2Y7detLVvG+pvt6LNXsSZhlU1rg41DEvhxMG7cU/31pHWa3u61W9Duu7z0ndxiXdqLeepJy7c8EFF7Bz507+/Oc/k5WlCru0eE3urZcBHSLUW0/Co0uXLpSXl2s+JpFG6JTfKhOSUZMoOUnKuDuff/45nTp14rrrrsPdlZxEJC6Bt6+YWcTMisws9XeaSbO66qqrGDx4MJ99Fp3oWIlJROIVhprTxcD7QPugA5HkGjt2LOXl5XTu3DnoUEQkzQRaczKzXsAJwF+CjEOSx90pKioCYMiQIdx0002qMYlIowXdrHc78Cug3mGdzex8M1tgZgtKSkqaLzJpkgcffJDBgwfz8ssvBx2KSFrSOS8qyMkGTwQ2uPtbZjayvv3cfSowFaJdyZspPGmiU089lY0bNzJixIigQxFJSzrnRQVZcxoBjDWz1cA/gKPN7MEA45EEPProo3z55Zfk5uZy8cUX614mEUlIYGcQd7/c3Xu5e1/gTOA/7h6uEUAlLosXL+bMM8/kD3/4Q9ChiEiGCENvPUlzBx54IM899xxHHnlk0KGISIYIRduLu7/g7icGHYc0ztSpU3n77bcBOProo8nO1ncdEUmOUCQnST9bt27lpptu4o477gg6FBHJQPqqK/WaUVTMNU+9y+fbyuvcvvP4q3k+rwN9L3t6t20Rg3ZtcqpH29Yo2CKNt3HrDqbNW7PHfTJ17D3VnKROM4qKufTxRbslpi/eeZbSVx7C3clu1wWL5NT5+gqH0rJyHCguLePyJxYzo6i4GSIXkUyg5CR1umX2Msordr/FYvv6ZWwvXgqVFXW8qn5l5RXcMntZssITkQynZj2p0/rSsl2ee2UFlhWh0+ifQMVOLNL4P53aZYqI1Ec1J6lTj4L/zVS79f2X+OSBX1BRthmzLCy7VcJliojsiZKT1OnS0QPIiUQHbM1qnU9Wm3ZYpGlJCSA3J1I9RbmISEPUrCd1Gn9ITzaXfs7vXl4P/QfTpt+hjRpdXL31RBKnmXBFapk7dy4/GT+ep556ipEjjws6HBFpYdSsJ3U69NBDOeusszjkkEOCDkVEWqAgp8xoA7wEtI7F8bi7XxVUPBK1aNEiDjjgADp27MjUqVOb7bhXzljMQ/PW4HFOENCzIJdvDizk+aUlrC8tU9OhSIYJsllvO3C0u39hZjnAK2Y2y93fCDCmFm3VqlUMGzaMyy+/nKuuSt33hCtnLOahN9aQyEQ1xaVlPPjGml2eX/7EYgAlKJEMEOSUGe7uX8Se5sSWFjuxVhj079+fP/zhD1x44YUpO8aVMxbzYIKJqT660VckcwTaIcLMIsBbwL7An9x9Xh37nA+cD9C7d8vstZJqr7zyCnvttRf77LMPkyZNSumxHp63NqXl60ZfSXc1z3lduvdscGy9VAhDD8FAk5O7VwAHm1kBMN3MDnD3JbX2Sdspi2cUFXPL7GWBXxOZcM/rvPrBxjq3eUU5xVN/SE6nnnQ74zoAImacNWxvrh9/YNJjqYj3olIT6UZfSXc1z3n99x+UVue8ZApFV3J3LzWzF4AxwJIGdk8LM4qKufyJxZSVR8egC+qayJ4SE4BFcuh66m+I5HaoXlfhXn09J9kJKmKWsgSlG31FMkdg15zMrDBWY8LMcoFjgKVBxZNst8xeVp2YqgRxTaS+xLR9/TK2LHoWgFaFfYm07bjbPqlogjtr2N5JLxOgIDeHm045UJ0hRDJEkPc57QU8b2bvAPOBOe4+M8B4kqq+ax9huSay+a0n2fzGY1SWb693n1TUcK4ffyD7dc1PermlZeVcMX0x/S57mhGT/6PpOUTSXGDNeu7+DpCxd3j2KMiluI5EFJZrIl2+dQkVX24hK6d1vftEGjFcUbyunLGYFRu2Jr1cgK07gm1CFZHk0QgRSTajqJgRk/9DcWkZtU/tQVwTGbFPp+rHOzasomTGTVTu+BLLziG7bac9vDI1TXCp7q1XRd3KRZomDD31QMkpqao6QVTVmByqE1TPgtxArok8NOmw6gRV/tlatn+8gsqyzXt8TcSM7wzvnZa99WoKSxOqiDReKHrrZYq6OkE40cT06mVHp/z4VV3XazcnVk0UmP/Vo8jdb/guTXkd83K46qSvNVvSTGVvvdrC0oQqIo2n5JREQXaCqN11vUr5f9ey4Ykb6HLCz2jdY8Bu15g+31bOpY8vAuq/PlNf0gszdSsXSW9q1kui+r6pN8c3+LpqbQCW04ZIbnuyWufV+9ryCq/3+kztpsowy28VwQiuCVVEkkc1pyS6dPSA3WovVd/gUz1aRO3aWcW2TWTltie7fSHdJtzc4ESB9dXu6kt6ydJcTZ4ikl6UnJKoKtnUTkJAykeL6JCbQ2lZOQAVWz/n4/t/TtuDjqNgxFlxzWDrQN/LngZgv675bNtRyfrSspSPxKtOCyLhkoqx/JrSA1DJKcnGH9Jzt4QzYvJ/6h0tIhnJaUZRMVu276x+npXXgfyvHkXefsOaVF6q7kOqS82kWJdU9RoUkXBTcmoGqe4occvsZVRUOjs3l2CRHCL5BXQceV5Syg5aqsb4E5FwU3JqBk0ZLWJGUTFXP/ludVNdQ7yygg2PX0NWq7y4rjGlk4fnrVVyEmlhgpymfW/g70B3oBKY6u6/DyqeZLlyxmIenreWCvfqqSf21FGiLjOKirn0sUWUV8Z/xceyInQ65nwsu3VGJSZo3ht3RSQcguxKvhP4hbvvDwwHfmJmXw0wnoRVzfJadTKtmnpiwUcbuemUA+lZkBtXV+dbZi+LOzFVfPE5ZaveAqBN70G07pF59/akYow/EQm3IAd+/Rj4OPZ4i5m9D/QE3gsqpkTV18tl2rw1XD8+/vtuGnMt6vMX/0rZinn0/PF9ZLVO/mjfYZCqaTZE0kFYxrprbqG45mRmfYmOUJ7W07TXV9lpROscUP81qrp0OuZHlB9yQtyJKTcni7LyysYFFCD11pOWpvY07S1V4MnJzNoC/wQucffdRiRN52nam+rS0QP2eM2pomwzm+fPoOCICWS1zqu3KS+SZUw57aAGa2xVo6jviUGT73nKAs4e3pt/vlW823U3jeQgsitN0x4V6PBFZpZDNDE95O5PBBlLMuTm1P1x1re+PuMP6cktpx1EQW5OndvLVr7JlvkzKC9ZXW8Z+a0icSUmiCbD3JxIvdtzcyJMGN67+ppZfqv69939tVncdsbBXD/+wEZddxORls08oJ5QFu1Sdj+w0d0viec1Q4YM8QULFqQ2sATMKCrm548spGajWRZw2xkHJ/0kPPXpedy/eGvShkOqObxSh9wczKB0W3lKhloSkd2me6tT//0H+ar330l1LM0trvceZHI6AngZWAzV5/Nfu/sz9b0m7MkJSNkYeps2beK8885j8uTJDBiQeT3yRFoYJacGBNlb7xXiDDKdVA1fNKOomGueepdLHlnIJY8spCA3h6vHNn3epE8++YQFCxawcuVKJScRyXiBd4jIRDOKirn08UWUV/yvVlpaVs6lj+153qS67Ny5k+zsbAYMGMDy5cvJzdUEeiKS+TSfUwrcMnvZLompSnll/fMm1WXbtm0cc8wx3HHHHQBKTCLSYig5pcCeumU35gbb7OxsunbtSrdu3ZIRlohI2lCzXgpEzOodDy6eWXHLysqoqKigbdu2PPLII00eK+/KGYuZNm9N9U3AuTlZ3HTKIPW8E5HQU80pBfY0UGlxaRl9L3uavpc9zYyi4t22uzunnXYaJ5xwAhUVFQklpgffWLPL6BRl5ZX8/JGFdR5XRCRMVHNKgZ5xDj90ySMLgV07SJgZ55xzDlu3biUSif9m19oenre2zvWVkLRJDkVEUkXJKQUuHT2gOvE0pCpR7Nixg2XLlnHggQdy+umn17lvzek4EqGp0UXSRyqmTW+sIAafVXJKgfGH9Iw7OVXVsP7v//6Pv/zlL6xYsYI3Pq7Y7UbeBR9trJ4VNlHxXPcSEQmSklPAjOh9Ub/61a849NBDeePjil0mJiwuLePyJxbzZY0BUxNVdd0rXvt1zWfOz0cm7fgiIg1Rh4gUaRNpuCODV1awZcl/+O2/l7LXXnsxceJEbpm9bJeRuwHKyiuaPCJ4MqzYsJVjb3shwAhEpKUJtOZkZvcBJwIb3P2AIGNpSLw1jdWTTwBg6Q3HM/CKZ/iyjptxq2x9/yX++/RtrGrbCRgFhPd60IoNW4MOQURakKBrTn8DxgQcQ4Ma0wRWc9+lNxzP6skncPsZB9Ozjus8+V8dSdczb6Bd/4Or19V3Pagx01SIiKS7QJOTu78EbAwyhlSbUVTM5U8sru744F5J6csPsfOLjZgZuX0OorwSJtzzOlD33Eq5ORFuOPlAvjO8N5Em3vckItIUQU0TH/oOEek0TXtdal9D2rlxPZsXzCArrz3tB59Uvf7VDzbWW0MrK6+o7v1Xu3NCVfKrfZ0q2fbrGt808CKSGE3THhV0s16D3H2quw9x9yGFhYVBh9NotW/Gzencix7f+xPtDj2xSeXV7pww/pCeu8ww2zEvJ+m/VPXWE2k+Nc957Qo6BR1OYEJfc0pnVU117s7nc++hdc+B5O9/JNkduiZU7ooNW3erZY3YpxOvXnZ0QuWKiIRF6GtOYVDVA6+x+776QfRymu/cwY5PP2DHpx8kPbaax6pKhiIi6S7Q5GRmDwOvAwPMbJ2ZfT/IePZk9eQTWD35hD12SsjNiewyqKq745UVZOW0ptsZ11Fw1HkpjbEqGYqIpLtAm/Xc/awgj99YVSN916esvIKrn3y3elDV0pfup/y/6ygcdxmW3aq5whQRSZqaY/s1Z889XXNqhHgGYCwtK6++HhTJ70Tl9m2QpXuUREQaQ8mpESrjHEOoYtsmInkdaD9kLO5e55xMWQa3nX7wHqeumFFUHPcAshDtFCEikgnUISLJNs//F+vvvYCdmzYA1DtZYDw3044/pCe3n3FwXL+kEft04qFJhzUmVBGR0FLNqRFyc7IoK6/c8z79B7NzSwmRdp33uF95pcc16d/4Q3pqYkARaXFUc2qEm04ZVO8Htv3jFUD0JttOR/8Ai+M6U1gHeRURCZpqTo1QVYOpfR1o2wfzKXn8GgpPuZK8/YbHXZ4m/RORoAU1dl5DVHNqpLqa2HL7HUrHY35Ibv8hcZeTmxPh0tEDkhmaiEjGUHJKwNZlr1K5fRuWFaH94JOwyJ4rolXj3/UsyOWmUw7UtSQRkXqoWa+RBl7xDAA7N2/gs6duof3Xx9MxjpEf1JtORCR+Sk6NcOxtL1TPbJvdvivdzryR1t33a/B1SkwiIo0T9DTtY4DfAxHgL+4+Och4GrJiw1a+WPIfstt1oU2fQbTp9dU97n/7GXu+yVZEROoWWHIyswjwJ+BYYB0w38yedPf3goqpIV6xk81vPkF2h6606TOowf2VmEQkEZ3yW4W2N12qBVlzGgqsdPdVAGb2D2AcENrkZJFsup15A5bTOuhQREQyWpC99XoCa2s8XxdbtwszO9/MFpjZgpKSkmYLrqbHH3+ciy66iH275BLJ60BWTpsGX5Pd8OhEIiK7CcM5LwyCTE51nb53G1o1DNO0FxUVUVRUxJMXDGe/rvkN7p9tsPKm+CcoFBGpEoZzXhgE2ay3Dti7xvNewPqAYqnTzp07yc7O5vrrr2f79u20adOGOT8fGXRYIiIZL8ia03xgPzPrZ2atgDOBJwOMZxfPPPMMgwYNYt26dZgZbdo03JQnIiLJEVjNyd13mtmFwGyiXcnvc/d3g4qnts6dO9OjRw/atm0bdCgiIi1O0NO0PwM8E2QMtZWUlFBYWMiwYcOYM2dOvfMxiYhI6mhsvRrefPNN+vfvz4wZM4D6JwoUEZHUUnKq4YADDuCcc85hxIgRQYciItKiKTkB77zzDtu3bycvL48//elPtOTumyIiYdDik1NJSQnf+MY3+MUvfhF0KCIiEtPiRyUvLCzkrrvuYuTIkUGHIiIiMS02OS1YsICcnBwOOuggzj777KDDERGRGlpkcqqsrOT73/8+OTk5zJ8/X73yRERCpkUmp6ysLKZPn05WVpYSk4hICLWoDhGLFy/mtttuA6B///707ds32IBERKROLSo53XvvvUyZMoWNGzcGHYqIiOxBIMnJzE4zs3fNrNLMhjTXcadMmcKbb75Jp06dmuuQIiLSBEHVnJYApwAvpfpAy5cvZ+zYsWzcuJFIJELPnpo6XUQk7AJJTu7+vrsva45jffjhhyxcuJANGzY0x+FERCQJQt9bz8zOB84H6N27d6NfP3r0aJYvX675mEQkLSR6zssUKas5mdlzZrakjmVcY8pJxpTFSkwiki40TXtUympO7n5MqsoWEZHM1qK6kouISHoIqiv5yWa2DjgMeNrMZgcRh4iIhFMgHSLcfTowPYhji4hI+KlZT0REQkfJSUREQkfJSUREQkfJSUREQkfJSUREQkfJSUREQkfJSUREQkfJSUREQsfcPegY4mZmJcBHTXhpF+CzJISQrHLCWpZiav6yFFPzlhOWsj5z9zEN7WRm/45nv0yUVsmpqcxsgbsnPONussoJa1mKqfnLUkzpG1Oyy5JdqVlPRERCR8lJRERCp6Ukp6khKyesZSmm5i9LMTVvOWEuS2poEdecREQkvbSUmpOIiKQRJScREQmdFpOczOw0M3vXzCrNrNFdP81sjJktM7OVZnZZAnHcZ2YbzGxJU8uIlbO3mT1vZu/H3tfFCZTVxszeNLNFsbKuSTC2iJkVmdnMBMtZbWaLzWyhmS1IsKwCM3vczJbGPrPDmlDGgFgsVctmM7skgZh+Fvu8l5jZw2bWponlXBwr493GxlPX36OZdTKzOWa2IvazYwJlNfr/rp5ybon97t4xs+lmVpBAWdfFylloZs+aWY+mlFNj2y/NzM2sSzwxSZzcvUUswP7AAOAFYEgjXxsBPgD6A62ARcBXmxjHkcChwJIE389ewKGxx+2A5QnEZEDb2OMcYB4wPIHYfg5MA2Ym+B5XA12S9Pu/H/hB7HEroCDB8iLAJ0CfJr6+J/AhkBt7/ihwXhPKOQBYAuQRndn6OWC/Rrx+t79H4LfAZbHHlwE3J1BWo//v6innOCA79vjmBGNqX+PxT4G7m1JObP3ewGyigwMk5W9VS3RpMTUnd3/f3Zc18eVDgZXuvsrddwD/AMY1MY6XgI1NjKNmOR+7+9uxx1uA94me8JpSlrv7F7GnObGlST1lzKwXcALwl6a8PhXMrD3Rk8u9AO6+w91LEyx2FPCBuzdlxJIq2UCumWUTTS7rm1DG/sAb7r7N3XcCLwInx/viev4exxFN5sR+jm9qWU35v6unnGdj7w/gDaBXAmVtrvE0nzj+1vfwf/s74FfxlCGN02KSU4J6AmtrPF9HExNBKphZX+AQojWeppYRMbOFwAZgjrs3tazbif6zVjY1lhoceNbM3jKz8xMopz9QAvw11tz4FzPLTzC2M4GHm/pidy8GbgXWAB8Dm9z92SYUtQQ40sw6m1kecDzRb/OJ6ObuH8fi/BjommB5yfY9YFYiBZjZDWa2FpgA/KaJZYwFit19USKxSN0yKjmZ2XOxtvfaS5NqOTWLrmNdKL4pmVlb4J/AJbW+ETaKu1e4+8FEv5EONbMDmhDLicAGd3+rqXHUMsLdDwW+BfzEzI5sYjnZRJtk7nL3Q4CtRJurmsTMWgFjgccSKKMj0RpKP6AHkG9m32lsOe7+PtFmrjnAv4k2Oe/c44vSmJldQfT9PZRIOe5+hbvvHSvnwibEkQdcQRMTmzQso5KTux/j7gfUsfwrwaLXseu30V40rQkmqcwsh2hiesjdn0hGmbHmrheApgw2OQIYa2ariTZ9Hm1mDyYQy/rYzw3AdKLNq02xDlhXozb4ONFk1VTfAt52908TKOMY4EN3L3H3cuAJ4PCmFOTu97r7oe5+JNGmpxUJxAXwqZntBRD7uSHB8pLCzM4FTgQmuHuyvhxOA77dhNftQ/SLxaLY33sv4G0z656kuFq8jEpOKTQf2M/M+sW+NZ8JPBlkQGZmRK+hvO/utyVYVmFV7yczyyV64lza2HLc/XJ37+XufYl+Rv9x90bXBmJx5JtZu6rHRC+IN6mHo7t/Aqw1swGxVaOA95pSVsxZJNCkF7MGGG5mebHf5Sii1w0bzcy6xn72Bk5JQmxPAufGHp8LJPrlLmFmNgb4P2Csu29LsKz9ajwdS9P+1he7e1d37xv7e19HtIPSJ4nEJjUE3SOjuRaiF4nXAduBT4HZjXz98UR7xH0AXJFAHA8TvcZQHovn+00s5wiiTYvvAAtjy/FNLGsQUBQrawnwmyR83iNJoLce0etEi2LLu4l85rHyDgYWxN7jDKBjE8vJA/4LdEjCZ3QN0RPjEuABoHUTy3mZaLJdBIxK9O8R6AzMJVoDmwt0SqCsRv/f1VPOSqLXfav+1hvsYbeHsv4Z+8zfAZ4CejalnFrbV6PeekldNHyRiIiEjpr1REQkdJScREQkdJScREQkdJScREQkdJScREQkdJScJOPERiC/oBmOM9LMmnTjrIjsmZKTZKICIO7kZFFN+V8YSRNHdRCRPdN9TpJxzKxq1PhlwPNEbzLuSHS09Svd/V+xwXJnxa6boC0AAAGgSURBVLYfRnTk7WOIjkKwnugNqNvd/UIzKwTuBnrHDnEJUEx0dOwKooPKXuTuLzfH+xNpCZScJOPEEs9Mdz+gajoKd98cmwzuDWA/oA+wCjjc3d+ITTj3GtEx97YA/wEWxZLTNOBOd38lNkTQbHff38yuBr5w91ub+z2KZLrsoAMQSTEDboyNaF5JdKqTbrFtH7n7G7HHQ4EX3X0jgJk9Bnwltu0Y4KvRIfAAaF817p+IpIaSk2S6CUAhMNjdy2MjSFdNh761xn51TYtSJQs4zN3Laq6skaxEJMnUIUIy0RaiU9cDdCA6x1S5mX2TaHNeXd4EjjKzjrGmwJrTKDxLjTl/zOzgOo4jIkmk5CQZx93/C7xqZkuIjkY+xMwWEK1F1Tk9gkdnpr2R6GzCzxEd5XtTbPNPY2W8Y2bvAT+KrX8KONnMFprZN1L2hkRaIHWIEIkxs7bu/kWs5jQduM/dpwcdl0hLpJqTyP9cbWYLic718yHReZ9EJACqOYmISOio5iQiIqGj5CQiIqGj5CQiIqGj5CQiIqGj5CQiIqHz/wFNRagsOz5GgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange + df.tile + df.unroll != 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df2 = df\n",
    "joint_plot(df2, f\"Validation dataset, {loss_func} loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
